*New* Open Discussion Slots: At Fuzz-IEEE 2019, select Special Sessions are organizing an ‘Open Discussion Slot’ (ODS). An Open Discussion Slot usually takes the shape of a standard (20 minute) paper presentation slot where proposers lead an open discussion on the topic of the special session with both presenting authors and session attendees. Open Discussion Slots, will usually be timetabled at the end of a special session to allow wider discussion around the special session topic in the context of the papers presented. For details on the ODS program put forward as part of the specific special sessions, please review the special session information for the respective special sessions below.
List of Special Sessions running an Open Discussion Slot:
- Software for Soft Computing
- Fuzzy Natural Language Processing
- Fuzzy Hybrid Computing Applications in Construction Engineering and Management
- Fuzzy Logic for Security and Forensics
- Advances on eXplainable Artificial Intelligence
- Recent Advances in Fuzzy Control System Design and Analysis
Submission of Papers to Special Sessions: Manuscripts submitted to special sessions should be submitted through the standard paper submission website of Fuzz-IEEE 2019, selecting the topic of the special session as primary topic. All papers submitted to special sessions will be subject to the same peer-review procedure as regular papers. Papers submitted to Special Sessions will be handled by both the Special Session and the Conference Program Chairs. The review of submissions of Special Session chairs and co-chairs to their own special session will be handled by the Conflict-of-Interest or Program Chairs. Note that special sessions with a very small number of accepted papers will either be merged with compatible special sessions or be cancelled and the accepted papers will be moved to regular sessions. All papers submitted to special sessions (if accepted and presented) will be published as part of the regular FUZZ-IEEE proceedings.
List of Special Sessions
Software for Soft Computing
Fuzzy Natural Language Processing
Fuzzy Hybrid Computing Applications in Construction Engineering and Management
The Theory of Type-2 Fuzzy Sets and Systems (T2-T)
Business Processes and Fuzzy Logic (BPFL)
Fuzzy Logic for Security and Forensics
Advances on eXplainable Artificial Intelligence
Linguistic Summarization and Description of Data
Advances to Type-2 Fuzzy Logic Control
Fuzzy System for Control and Diagnosis of Renewable Energy and Smart Grid
Soft Computing in Computer Vision and Pattern Recognition
Human Symbiotic Systems
Adaptive Fuzzy Control for Nonlinear Systems
Recent Advances in Evolving Fuzzy Systems
Recent Advances in Fuzzy Control System Design and Analysis
Handling Uncertainties in Big Data by Fuzzy Systems
Biologically-inspired Intelligence based Fuzzy Logic for Robotics and Mechatronics
Special Session on Pre-Aggregation Functions and Generalized Forms of Monotonicity
Special Session on Applications of Pre-Aggregation Functions
Organized by Jose M. Alonso (firstname.lastname@example.org), Jesús Alcalá-Fdez, and Jose Manuel Soto-Hidalgo
The term Soft Computing is usually used in reference to a family of several preexisting techniques (Fuzzy Logic, Neuro-computing, Probabilistic Reasoning, Evolutionary Computation, etc.) able to work in a cooperative way, taking profit from the main advantages of each individual technique, in order to solve lots of complex real-world problems for which other classical techniques are not quite well suited.
In the last few years, many software tools have been developed for Soft Computing. Although a lot of them are commercially distributed, unfortunately only a few tools are available as open source software (see the webpage http://sci2s.ugr.es/fss). In the field of evolutionary computation, JCLEC (Java Class Library for Evolutionary Computation), KEEL (Knowledge Extraction based on Evolutionary Learning), and JMetal (Metaheuristic Algorithms in Java) provide nice examples of frameworks for both evolutionary and multi-objective optimization. JavaNNS (Java version of Stuttgart Neural Network Simulator) is probably the best free suite for neural networks. Regarding fuzzy modeling, JFML (the first library in the world that allows to develop fuzzy systems according to the new IEEE Std 1855 published and sponsored by the Standards Committee of the IEEE Computational Intelligence Society), Xfuzzy (a development environment for fuzzy-inference-based systems), FisPro (Fuzzy Inference System Professional), and GUAJE (Generating Understandable and Accurate fuzzy models in a Java Environment) represent very useful tools. Regarding neuro-fuzzy algorithms we can point out to NEFCLASS (Neuro-Fuzzy Classification). Finally, FrIDA (Free Intelligent Data Analysis Toolbox) and KNIME (Konstanz Information Miner) are examples of user-friendly open-source software which offer several individual tools for data processing, analysis and exploration/visualization. Please, notice that such open tools have recently reached a high level of development. As a result, they are ready to play an important role for industry and academia research.
The aim of this session is to provide a forum to disseminate and discuss Software for Soft Computing, with special attention to Fuzzy Systems Software. We want to offer an opportunity for researchers and practitioners to identify new promising research directions in this area.
Further details about the session are at:
Scope and Topics:
- Data Preprocessing
- Data Mining and Evolutionary Knowledge Extraction
- Modeling, Control, and Optimization
- System Validation, Verification, and Exploratory Analysis
- Knowledge Extraction and Linguistic/Graphical Representation
- Visualization of Results
- Languages for Soft Computing Software
- Data Science, Big Data, and High Performance Computing (Map-Reduce, GPGPU, etc.)
Organized by Keeley Crockett (K.Crockett@mmu.ac.uk) and Joao Paulo Carvalho
Although language, or linguistic expressions, undoubtedly contains fuzziness in nature, very little research has been conducted in related fields in recent years, as it was shown in “A Critical Survey on the use of Fuzzy Sets in Speech and Natural Language Processing”, Proc. of the IEEE WCCI 2012, Brisbane, Australia. This is partly because of the prevalence of probabilistic machine learning technologies in the natural language processing field. However, there has been a growing recognition that fuzziness found in every aspect of human language has to be adequately captured and that recent developments in the fields of computational intelligence such as computing with words can make a contribution. This session will follow on from the successful, special session entitled “Fuzzy Natural Language Processing” which was held at IEEE FUZZ 2017 in Naples, and in Istanbul and IEEE FUZZ 2013 in India and the hybrid special sessions held at the 2014 IEEE WCCI in Beijing, 2016 IEEE WCCI in Vancouver and 2018 IEEE WCCI Brazil.
The aim of this Special Session is therefore to explore new techniques and applications in the field of fuzzy natural language processing which capture the fuzzy nature of human language.
The session will provide a forum to disseminate and discuss recent and significant research efforts in fuzzy paradigms and applications in the field of fuzzy natural language processing.
Scope and Topics:
- fuzzy set models of human language
- fuzzy logic applications to human language processing
- fuzzy machine learning approach to human language
- fuzzy text and social media mining
- fuzzy simulations of language use
- fuzzy ontology for human language
- fuzzy applications to the semantic web
Computing with words within natural language processing
- Real world computational intelligence inspired natural language processing applications
- Computational intelligence founded methodologies, tools and techniques for mining and interpretation of social media textual data
Organized by Aminah Robinson (email@example.com)
The construction industry is a vital part of many national economies. Construction industry performance is largely dependent on the effective planning, execution, and control of construction projects, which involve both complexity and uncertainty. Fuzzy logic methodologies are able to model subjective information, handle uncertainty and complexity, and address the lack of comprehensive data sets available for modeling in construction engineering and management. In recent years, researchers have combined fuzzy logic with other soft computing and simulation techniques to create advanced fuzzy hybrid systems that are well suited to construction modeling. This session focuses on recent advances and applications of fuzzy hybrid computing techniques in the construction domain. The practical application of these techniques to solve real-world problems across a wide range of construction engineering and management issues will be discussed.
Scope and Topics:
- Fuzzy Logic in Construction
- Fuzzy Hybrid Techniques in Construction
- Fuzzy Arithmetic Applications in Construction
- Fuzzy Simulation Techniques in Construction
- Fuzzy Machine Learning and Optimization Techniques in Construction
- Fuzzy Multi-Criteria Decision Making Applications in Construction
- Neuro-fuzzy Inference System Applications in Construction
- Soft Computing for Construction Applications, including Risk Analysis, Project Performance, Productivity, Procurement, Contracting Strategies, Construction Methods, Competency Assessment, Quality Management, and Project Planning and Control
Organized by Robert John (firstname.lastname@example.org), Josie McCulloch, and Simon Coupland,
Type-2 fuzzy sets and systems are paradigms which seek to realize computationally efficient fuzzy systems with the ability to give excellent performance in the face of highly uncertain conditions. Specifically, type-2 fuzzy sets provide a framework for the comprehensive capturing and modelling of uncertain data, which, together with approaches such as clustering and similarity measures (to name but two) provides strong capability for reasoning about and with uncertain information sources in a variety of contexts and applications. Type-2 fuzzy systems combine the potential of type-2 fuzzy sets with the strengths of rule-based inference in order to provide highly capable inference systems over uncertain data which remain white-box systems (i.e. interpretable). The aim of this special session is to present and focus top quality research in the areas related to the underlying theory of type-2 fuzzy sets and systems. There are many open and unanswered questions about properties and nature of type-2 fuzzy sets and systems, this session is designed to provide a forum for the academic and industrial communities to report on advances
Scope and Topics:
- Representations of type-2 fuzzy sets
- Approaches to defuzzification
- Fuzzy operators
- Fuzzy measures
- Computational complexity
- Related extensions to type-1
Organized by Mario Luca Bernardi (email@example.com), Marta Cimitile, and Giuseppe De Ruvo
This session is at its third edition in Fuzz-IEEE.
Business processes are a formal and well-defined representation of enterprise activities, capable of highlighting, in a structured way, how multiple complex tasks are performed within a given organization. This representation enables a more clear understanding of interactions occurring within a company with a consequent and improved collaboration among all the stakeholders involved in a given technical or administrative process. In the last years, several techniques for business process management have been introduced to efficiently discover, monitor, and execute business processes in different enterprise scenarios. However, these techniques could fail when processes are not carefully designed and optimized, when they quickly change over time or when they refer to highly uncertain and vague contexts. Consequently, fuzzy logic and approximate rule-based reasoning can efficiently support tool for business process management in facing aforementioned challenges.
Specifically, fuzzy theory could play an important role for clustering, data analysis, data fusion, pattern recognition, modeling, multi-criteria evaluation and, more in general, for several business intelligence approaches. Fuzzy theories can be also combined with other techniques such as neural nets and evolutionary computing and applied to both business process design and management approaches as well as to complex process analysis focused on the extraction and representation of hidden knowledge.
The objective of this special session is to provide a forum for the discussion of recent research trends in the application of fuzzy set methodology and technology to business process management problems and to offer an opportunity for researchers and practitioners to identify and discuss about new promising research directions in this challenging scenario.
Scope and Topics:
- Fuzzy Logic for Adaptive and Context-Aware process execution
- Fuzzy Logic for effective analytics and visualization of enterprise processes
- Fuzzy models supporting business process management approaches
- Offline and Online process mining approaches dealing with uncertainty
- Fuzzy-based qualitative and quantitative process analysis
- Using Fuzzy theories to for process querying, refactoring, searching and versioning
- Fuzzy models to represent process data
- Fuzzy models to perform process integration
- Fuzzy models and data mining approaches for process management
- Fuzzy-based frameworks specific for business process representation and modeling
- Case studies and empirical evaluations
Organized by Longzhi Yang (firstname.lastname@example.org), Nitin Naik, Paul Jenkins, Leslie Sikos, and Natthakan Iam-On
Computational intelligence (CI) has taken the centre of cybersecurity and digital forensics. Being an important parts of CI, fuzzy logic has been successfully applied in many real-world applications. Thanks to its ability to provide human-comprehensible solutions to cybersecurity and forensics problems in uncertain environments and the fuzziness of the security and forensics problems themselves, fuzzy logic is expected to have a more significant impact in this field.
The aim of this special session is to provide a forum to (1) disseminate and discuss contemporary and significant research efforts in the field of fuzzy logic, security, and forensics; (2) promote both theoretical development and practical applications of fuzzy logic in the field of security and forensics; and (3) foster the integration of communities from academic, industry, and other organisations working in the field of fuzzy logic, security, and forensics.
Scope and Topics:
- Fuzzy logic for cybersecurity
- Fuzzy logic for digital forensics
- Fuzzy logic for cyber-privacy
- Fuzzy logic for systems security
- Fuzzy logic for forensics
- Fuzzy logic for biometrics
- Fuzzy logic for big data security
- Fuzzy logic for cloud computing security
- Fuzzy logic for IoT security
- Fuzzy logic for web security
- Fuzzy logic for software security
- Fuzzy logic for dependable systems
- Fuzzy logic for cyber-physical systems
Organized by Jose M. Alonso (email@example.com), Ciro Castiello, and Corrado Mencar
In the era of the Internet of Things and Big Data, data scientists are required to extract valuable knowledge from the given data. They first analyze, cure and pre-process data. Then, they apply Artificial Intelligence (AI) techniques to automatically extract knowledge from data.
Our focus is on knowledge representation and how to enhance human-machine interaction. As remarked in the last challenge stated by the USA Defense Advanced Research Projects Agency (DARPA), “”even though current AI systems offer many benefits in many applications, their effectiveness is limited by a lack of explanation ability when interacting with humans””. Accordingly, users without a strong background on AI, require a new generation of explainable AI systems. They are expected to naturally interact with humans, thus providing comprehensible explanations of decisions automatically made. This is also aligned with the European vision for AI (CLAIRE) which remarks the need of building trustworthy AI that is beneficial to people through fairness, transparency and explainability. Thus, the goal of this special session is to discuss and disseminate the most recent advancements focused on explainable artificial intelligence. The session goes a step ahead with respect to the previous events we organized (which were mainly focused on interpretable fuzzy systems) in some other conferences: joint IFSA-EUSFLAT 2009, ISDA 2009, WCCI 2010, WILF 2011, ESTYLF 2012, WCCI 2012, EUSFLAT 2013, IFSA-EUSFLAT2015, FUZZ-IEEE2017, and IPMU2018.
The aim of this session is to provide a forum to disseminate and discuss eXplainable Artificial Intelligence, with special attention to Interpretable Fuzzy Systems. We want to offer an opportunity for researchers and practitioners to identify new promising research directions in this area.
Further details about the session are at
Scope and Topics:
- Explainable Computational Intelligence
- Theoretical Aspects of Interpretability
- Dimensions of Interpretability: Readability versus Understandability
- Learning Methods for Interpretable Systems and Models
- Interpretability Evaluation and Improvements
- Relations between Interpretability and other Criteria (such as Accuracy, Stability, Relevance, etc.)
- Design Issues
- Successful Applications of Interpretable AI Systems
- Interpretable Fuzzy Systems
- Interpretable Machine Learning
- Models for Explainable Recommendations
- Explainable Agents
- Self-explanatory Decision-Support Systems
- Argumentation Theory for Explainable AI
- Natural Language Generation for Explainable AI
- Interpretable Human-Machine Interaction
Organized by Daniel Sanchez (firstname.lastname@example.org), Anna Wilbik, and Nicolas Marin
The development of human–computer interaction systems based on natural language, already important in the last decades, is growing in importance nowadays. Particularly, data-to-text systems are intended to obtain a text describing the most relevant aspects of data for a certain user in a specific context. Such texts, called linguistic summaries and descriptions of data, are comprised of a collection of natural language sentences, and must be as close as possible to those generated by human experts. In this realm, not only specialized users (e.g. in decision support systems) are interested in this type of approach, but nonspecialized users also show interest in receiving understandable information that is supported by data.
Linguistic summaries commonly use fuzzy set theory to model linguistic variables and incorporate different forms of imprecision in a collection of natural language sentences. In many approaches they can be considered as quantifier based sentences, hence linguistic summaries constitute a perfect application for new developments in the domain of fuzzy quantifiers. Furthermore, linguistic summaries have been related to fuzzy rule systems.
Linguistic summaries and description of data is related to other research areas such as knowledge discovery in databases and intelligent data analysis, flexible query answering systems for data, human-machine interaction, uncertainty management, heuristics and metaheuristics, and natural language generation and processing. More recently, this field has been related to the linguistic description of complex phenomena and computing with words paradigms.
The objective of this special session is to provide a forum for researchers, from the above indicated areas, to present recent developments in linguistic summarizes and description of data as well as discuss how these different approaches can complement each other for the task of building such systems.
The session continues the series of special sessions on the topic organized by some of the organizers of this session in past conferences (IFSA 2015, FUZZ-IEEE 2015, FUZZ-IEEE 2016, FUZZ-IEEE 2017, FUZZ-IEEE 2018) and is supported by IEEE CIS task force on Linguistic Summaries and Description of Data.
Scope and Topics:
- Protoforms and fuzzy concepts for the linguistic summaries and fuzzy description
- Referring expression generation with fuzzy properties
- Quality assessment of linguistic summaries and fuzzy description
- Techniques and algorithms for generating linguistic summaries and descriptions of data
- Ontologies for data summarization
- Logical approaches for modeling linguistic expressions
- Modeling uncertainty for linguistic summaries and fuzzy description
- User preference/interest modeling for linguistic summaries and fuzzy description
- Applications of linguistic summaries and fuzzy description
- Natural language generation for data summarization
- Machine Learning applied to data summarization
- Linguistic information extraction from visual information
- Context-awareness in data summarization and description, and natural language generation
Organized by Tufan Kumbasar (email@example.com), Erdal Kayacan, Hao Ying
Type-2 fuzzy logic control is a technology which takes the fundamental concepts in control from type-1 fuzzy logic and expands upon them in order to deal with higher levels of uncertainty presented in many real-world control problems. A variety of control application areas have been addressed with type-2 fuzzy logic, from the control in steel production plants to the control of marine diesel engines and robotic control. For some engineering applications, there is evidence that type-2 fuzzy logic can provide benefits over both traditional forms of control as well as type-1 fuzzy logic. It is the aim of this special session to present a comprehensive selection of high-quality, representative current research in the area of type-2 control, motivating further collaboration and providing a platform for the discussion on future directions of type-2 fuzzy logic control. This special session will focus on advances in the interval type-2 as well as general type-2 fuzzy logic control.
Scope and Topics:
- Interval Type-2 Fuzzy Logic Control
- General Type-2 Fuzzy Logic Control
- Type-2 TSK Fuzzy Logic Control
- PID type Type-2 Fuzzy Logic Control
- Model-Based Type-2 Fuzzy Logic Control
- Adaptive / Self-Tuning Type-2 Fuzzy Control
Neuro-Fuzzy Type-2 Control
- Interpretability of Type-2 Fuzzy Controllers
- Real-time applications of Type-2 Fuzzy Controllers
- Deep Learning based Type-2 Fuzzy Controllers
Organized by Marco Mussetta (MARCO.MUSSETTA@POLIMI.IT), Horst Schulte (firstname.lastname@example.org), Faa-Jeng Lin, Kevin Guelton, and Francesco Grimaccia
The main aim of this session is to provide a forum for researchers, from both the academia and industry, covering the state-of-the-art methods and results in the area of fuzzy systems applications to analysis, control and diagnosis of renewable power generation system and Smart Grid (SG).
Renewable Energy System are characterized by the ability to transform fluctuating renewable energy sources (RES) into storable, transformable and scalable energy forms. SG technology employ information, communication, and automation technology to deploy an integrated power grid with smart power generation, transmission, distribution and RES integration.
Due to the climate goals, the research activities in the area of method development for the control of RES have increased enormously in recent years. Advanced Fuzzy Control techniques enable a model-based flexible controller design with mixed objectives. In addition, since the main RES are fluctuating (such as wind and solar energy, wave energy and tidal energy), accurate predictions and modeling of wind speed and solar insolation are necessary. Moreover, grid integration and the SG concept include smart meters, EV charging stations and energy management system as key enabling factor toward the Smart City concept.
As a result, effective application of computational intelligence techniques such as Fuzzy Systems can provide improved results and more methodical development in the analysis, control and diagnosis of renewable power generation in a grid-connected system. Extension to consider hybrid systems and distributed systems is possible within this framework.
The session continues the series of special sessions on these topics organized in past conferences (FUZZ-IEEE 2011, WCCI 2012, FUZZ-IEEE 2013, WCCI 2014, WCCI 2016, FUZZ-IEEE 2017, WCCI 2018) and is supported by the IEEE CIS Task Forces on “Fuzzy Systems in Renewable Energy and Smart Grid” and “Fuzzy Control Theory and Application”.
Scope and Topics:
- Fuzzy modeling and control of renewable energy sources (RES)
- Fuzzy energy management systems and distribution systems automation
- Fuzzy power quality, protection and reliability analysis of power system
- Fuzzy observer-based fault diagnosis and FTC of RES
- Fuzzy based health monitoring of large-scale mechanical structures of RES
- Fuzzy modeling and control of distributed virtual power plants
- Takagi-Sugeno fuzzy modeling of RES
- Stability Analysis of RES connected to the grid based on TS fuzzy systems
- TS Sliding mode control and observer design for RES
- Type-2 fuzzy control for RES
- Prediction of renewable energy using fuzzy and neuro-fuzzy systems
- Fuzzy Logic application for Demand-Response in Smart Buildings, Smart Grid and Smart Cities
- Hybrid systems of computational intelligence techniques in Micro-Grids and Smart Grids
- Novel applications in electric energy market
Organized by Stanton Price (email@example.com) and Chee Seng
Soft computing in computer vision and pattern recognition encompasses a broad array of topics critical to numerous real-world applications. Many modern technologies utilize, in some form, machine learning algorithms to achieve desired performance in computer vision, and pattern recognition applications. One of the major goals of machine learning is to match the reasoning and inference of a machine to that of humans, which will likely lean heavily upon being coupled to soft computing methodologies (e.g., Fuzzy Logic). Computer vision has seen advancements through soft computing in many ways, such as soft feature extraction, fuzzy rule-based (inference) image classification, multi source fusion, etc. Novel fuzzy approaches have been put forth for the task of pattern recognition, for example, clustering via fuzzy c-Means, cluster validity, and fuzzy vectors are all fuzzified techniques that have enhanced the field of pattern recognition.
The aim of this special session is to present highly technical papers describing new soft/fuzzy approaches to computer vision and pattern recognition. Though this session is broad, its topics are highly intertwined and its implications are vast to both academia and industry. It is dedicated to providing its participants an opportunity to present and identify new promising research in these areas.
Scope and Topics:
- Soft Computing in Computer Vision
- Soft Computing in Deep Learning
- Fuzzy Evolutionary Algorithms in Pattern Recognition and Computer Vision
- Fuzzy Optimization Algorithms in Pattern Recognition and Computer Vision
- Fuzzy Classification
- Data Fusion
- Uncertainty in Object Detection and Classification
- Advances in Soft Feature Extraction
- Architectures for Machine Learning
- Image Processing and Classification
- Pattern Recognition
- Fuzzy Kernels
- Multiple Kernel Learning
- Predictive Data Analytics
- Data Preprocessing
- Data Mining
Organized by Tomohiro Yoshikawa (firstname.lastname@example.org), Yoichiro Maeda, and Daisuke Katagami
This special session aims at discussing the basic principles and methods of designing intelligent interaction with the bidirectional communication based on the effective collaboration and symbiosis between the human and the artifact, i.e. robots, agents, computer and so on relating to fuzzy theory.
We aims at encouraging the academic and industrial discussion about the research on Human-Agent Interaction (HAI), Human-Robot Interaction (HRI), and Human-Computer Interaction (HCI) concerning Symbiotic Systems. Reflecting the fact that this society covers a wide range of topics, in this session we invite not only fuzzy researchers but also the related researchers from a variety of fields including intelligent robotics, human-machine interface, Kansei engineering and so on.
Scope and Topics:
- Human-Agent Interaction (HAI)
- Human-Robot Interaction (HRI)
- Human-Computer Interaction (HCI)
- Social Communication or Interaction
- Partner or Communication Robots
- Hospitality Robots
- Human Interface Systems
- Cooperative Intelligence
- Kansei Engineering
Organized by Valentina Emilia Balas (email@example.com), Tsung-Chih Lin, and Rajeeb Dey
The aim of this special session is to present the state-of-the-art results in the area of adaptive intelligent control theory and applications and to get together researchers in this area. Adaptive control is a technique of applying some methods to obtain a model of the process and using this model to design a controller. Especially, fuzzy adaptive control has been an important area of active research. Significant developments have been seen, including theoretical success and practical design. One of the reasons for the rapid growth of fuzzy adaptive control is its ability to control plants with uncertainties during its operation.
The papers in this special session present the most advanced techniques and algorithms of adaptive control. These include various robust techniques, performance enhancement techniques, techniques with less a-priori knowledge and nonlinear intelligent adaptive control techniques. This special session aims to provide an opportunity for international researchers to share and review recent advances in the foundations, integration architectures and applications of hybrid and adaptive systems.
Scope and Topics:
- Fuzzy Self-Organizing Controllers
- Adaptive Fuzzy Control Design
- Fuzzy Applications
- Fuzzy Modeling and Simulation
- Fuzzy Model Reference Learning Controller
- Hybrid adaptive fuzzy control
- Robust adaptive fuzzy control
- Adaptive fuzzy sliding-mode control
- Time-Delay Nonlinear Systems
- Adaptive and learning control theory
- Adaptive control of processes
- Data based auto-tuning of the controller
- Estimation and identification and its application to control design
- Cooperative Control
- Hybrid Intelligent Control
Organized by Igor Škrjanc (firstname.lastname@example.org), Fernando Gomide, and Edwin Lughofer
Evolving systems are modular systems that simultaneously develop their structure, functionality, and parameters in a continuous, self-organized, one pass adaptive way from data streams.
During the last 12-15 years, the concept of Evolving Fuzzy Systems (EFS) established as a useful and necessary methodology to address the problems of imprecision, incremental learning, adaptation and evolution of fuzzy systems in dynamic environments and during on-line/real-time operation modes. EFS are able to automatically and autonomously adapt themselves to new operating conditions and system states and hence guarantee a high process safety, especially in case of highly dynamic and time-variant systems. This is especially necessary when precise and sufficient training data is not available (e.g., because of high costs for data collection or annotation) in order to set up models which cover the whole range of possible system states. In other cases, drifts or shifts in the systems may appear (due to environmental changes or changes in system modes and interrelations), which cannot be appropriately handled with standard fuzzy systems learning methods. EFS significantly contributes in this field of research by assuring flexible models and robustly outweighing of older learned behaviors smoothly over time. Another major topic which can be addressed with EFS are the building of models from huge massive stream data or even from Big Data, and to serve as dynamically adaptable knowledge base within enriched human-machine interaction applications (learning and teaching).
The goal of the special session is to provide a broad picture of the recent developments and to explore further (open) research challenges in one or several specific research topics mentioned below.
Scope and Topics:
- Novel adaptive, incremental methods in evolving fuzzy modeling tasks
Evolving fuzzy classifiers (using different model architectures)
- Evolving Takagi-Sugeno-Kang type fuzzy systems
- Evolving neuro-fuzzy approaches
- Evolving type-2 fuzzy systems and related architectures
- Evolving modeling and control systems
- Data stream fuzzy clustering (in various forms)
- Adaptive fuzzy pattern recognition
- Adaptive fuzzy regression and correlation techniques
- Hybridizations of evolving fuzzy systems with incremental machine learning and data mining techniques
- Enhanced Issues in dynamic fuzzy methods
- Issues on robustness, stability and process-safety in evolving fuzzy systems
- Evolving techniques to address concept drift and shift
- Evolving fuzzy models in soft sensing
- On-line techniques to deal with model uncertainty and interpretability issues
- Active and semi-supervised learning with fuzzy concepts
- On-line and evolving design of experiments
- On-line dimensionality reduction and feature selection
- On-line complexity reduction and model transparency assurance issues
- Dynamic split-and-merge techniques for fuzzy rules
- Evolving granular modeling and control
- Towards plug-and-play capability
- Real-World applications of evolving fuzzy systems
- On-line system identification
- On-line fault detection and decision support diagnosis
- Data stream mining and adaptive knowledge discovery
- Database and web mining
- Big Data
- Control and decision support systems
- Image classification and visual Inspection
- Automation and robotics
- Control systems
- Data stream mining and adaptive knowledge discovery
- Forecasting in financial domains and time-series prediction
- On-line condition monitoring and predictive maintenance
Organized by Kevin Guelton (email@example.com), Zsófia Lendek, and Jun Yoneyama
Fuzzy models have been employed for the analysis and design of a wide range of nonlinear control systems. As confirmed by the large number of results being published in the literature, fuzzy control systems provide a systematic and efficient approach to the analysis and control of nonlinear processes. However, new techniques and the improvement of current ones are constantly required, fueled by the need for more and more efficient control of nonlinear systems.
Aim and scope:
The aim of this special session is to present the state-of-the-art results in the area of theory and applications of fuzzy control system design and analysis, and to get together well-known and potential researchers in this area, from both the academia and industry. In the proposed special session, the focus is mainly on the fuzzy control system design and analysis with emphasis on the theory and applications. The important problems and difficulties on the fuzzy control systems will be addressed, their concepts will be provided and methodologies will be proposed to take care of the nonlinear systems using the fuzzy control system approaches.
Scope and Topics:
- Takagi-Sugeno fuzzy control system
- Uncertain fuzzy system
- Fuzzy hybrid system
- Fuzzy switching system
- Fuzzy time-delay system
- Fuzzy stochastic system
- Fuzzy polynomial system
- Stability analysis of Takagi-Sugeno fuzzy system
- Nonlinear control design based on Takagi-Sugeno fuzzy system
- Predictive control
- Robust control
- Sampled-data control
- Sliding mode control and observer
Organized by Guangquan Zhang (Guangquan.Zhang@uts.edu.au), Hua Zuo, and Jie Lu
The volume, variety, velocity, veracity and value of data and data communication are increasing exponentially. The “Five Vs” are the key features of big data, and also the causes of inherent uncertainties in the representation, processing, and analysis of big data. Also, big data often contain a significant amount of unstructured, uncertain and imprecise data.
Fuzzy sets, logic and systems enable us to efficiently and flexibly handle uncertainties in big data in a transparent way, thus enabling it to better satisfy the needs of real world big data applications and improve the quality of organizational data-based decisions. Successful developments in this area have appeared in many different aspects, such as fuzzy data analysis technique, fuzzy data inference methods and fuzzy machine learning. In particular, the linguistic representation and processing power of fuzzy sets is a unique tool for bridging symbolic intelligence and numerical intelligence gracefully. Hence, fuzzy techniques can help to extend machine learning in big data from the numerical data level to the knowledge rule level. It is therefore instructive and vital to gather current trends and provide a high-quality forum for the theoretical research results and practical development of fuzzy techniques in handling uncertainties in big data.
This special session aims to offer a systematic overview of this new field and provides innovative approaches to handle various uncertainty issues in big data presentation, processing and analysing by applying fuzzy sets, fuzzy logic, fuzzy systems, and other computational intelligent techniques. The scope of this special session includes, but not limited to, fuzzy rule-based knowledge representation in big data processing, unstructured big data visualization, granular modelling, uncertain data presentation and modelling in cloud computing, Information uncertainty handling in recommender systems, real world cases of uncertainties in big data, etc.
Scope and Topics:
- Fuzzy rule-based knowledge representation in big data processing
- Information uncertainty handling in big data processing
- Unstructured big data visualization
- Uncertain data presentation and fuzzy knowledge modelling in big data sets
- Tools and techniques for big data analytics in uncertain environments
- Computational intelligence methods for big data analytics
- Techniques to address concept drifts in big data
- Methods to deal with model uncertainty and interpretability issues in big data processing
- Feature selection and extraction techniques for big data processing
- Granular modelling, classification and control
- Fuzzy clustering, modelling and fuzzy neural networks in big data
- Evolving and adaptive fuzzy systems in in big data
- Uncertain data presentation and modelling in data-driven decision support systems
- Information uncertainty handling in recommender systems
- Uncertain data presentation and modelling in cloud computing
- Information uncertainty handling in social network and web services
- Real world cases of uncertainties in big data
Organized by Chaomin Luo (Chaomin.Luo@ece.msstate.edu),
Integrated technique of biologically-inspired intelligence and fuzzy logic, an important embranchment of series on computational intelligence, plays a crucial role for robotics. The autonomous robot and vehicle industry has had an immense impact on our economy and society, and this trend will continue with fuzzy logic and its hybridizations with biologically inspired intelligence techniques. Biologically-inspired intelligence, such as biologically-inspired neural networks (BNN), is about learning from nature, which can be applied to the real world robot and vehicle systems. Recently, the research and development of incorporation of bio-inspired systems and fuzzy logic for robotic applications is increasingly expanding worldwide. Recently, fuzzy logic technique has effectively integrated with biologically-inspired algorithms including emerging sub-topics such as bio-inspired neural network algorithms, brain-inspired neural networks, swam intelligence, ant colony optimization algorithms (ACO), bee colony optimization algorithms, particle swarm optimization, immune systems, and biologically-inspired evolutionary optimization, etc. Additionally, it is decomposed of computational aspects of bio-inspired system based fuzzy system applications such as machine vision, pattern recognition for robot and vehicle systems, motion control, motion planning, movement control, sensor-motor coordination, and learning in biological systems for robot and vehicle systems.
This special session seeks to highlight and present the growing interests in emerging research, development and applications in the dynamic and exciting areas of biologically-inspired algorithm based fuzzy logic for robot and vehicle systems (autonomous robots, unmanned underwater vehicles, and unmanned aerial vehicles).
Original research papers are solicited in related areas of biologically-inspired algorithm based fuzzy logic for robotics. Submissions to the Special Session should be focused on theoretical results or innovative applications of biologically-inspired algorithms (such as BNN) associated with fuzzy logic for robot and vehicle systems.
Scope and Topics:
- Biologically-inspired neural networks and fuzzy logic for robotics
- Deep neural networks, learning systems and fuzzy logic for robotics such as motion planning, navigation, mapping, localization, and image processing, etc
- Bio-inspired system based fuzzy logic on computer vision and image progressing for robotics
- Human-like learning based fuzzy logic for robotics
- Neuro-dynamics based fuzzy models for robot and vehicle systems
- Evolutionary optimization based fuzzy logic for robot and vehicle systems
- Brain-inspired neural networks based fuzzy logic for robotics
- Swarm intelligence based fuzzy logic for robotics
- Evolutionary neuro-fuzzy for robot and vehicle systems
- Bio-inspired neuro-fuzzy system on machine learning, intelligent systems design for robotics
- Cellular automata and fuzzy logic for robotics
- Immune systems based fuzzy logic for robotics
- Ant colony optimization algorithms (ACO) with fuzzy logic for robotics
- Bee colony optimization algorithms (BCO) with fuzzy logic for robotics
Organized by Humberto Bustince (firstname.lastname@example.org), Javier Fernandéz, and Graçaliz Dimuro
This session will discuss the recent theoretical developments in the field of fusion functions when generalized forms of monotonicity are considered. More specifically, this session will focus on weaker forms of monotonicity, like directional monotonicity and ordered directional monotonicity. From those concepts, the session will cover important generalizations of the concept of aggregation functions, like pre-aggregation functions and pre-classes of functions. Pre-aggregation functions have appeared in the literature in recent years and lead to new classes of functions that encompass both classical aggregation functions and other weaker functions that do not fulfil the full monotonicity condition required to an aggregation, but presenting excellent behavior in aggregation processes, so offering more flexibility in applications.
In this sense, the session aims at providing researchers in the field with an opportunity to present their most recent developments and for discussing recent trends in this area, as well as to identify potential problems of interest for researchers.
Scope and Topics:
- Directional monotonicity
- Weak monotonicity
- Ordered directional monotonicity
- Pre-aggregation functions
- Pre-classes of functions
Organized by Humberto Bustince (email@example.com), Graçaliz Dimuro, and Benjamín Bedregal
This session will discuss the potential applications of pre-aggregation functions and other classes of aggregation-like functions satisfying weaker monotonicity conditions, like directional monotonicity and ordered directional monotonicity. Pre-aggregation functions have appeared in the literature in recent years for applications in classification problems, replacing classical aggregation operators in the fuzzy reasoning mechanism of fuzzy rule-based systems (FRBS). The excellent performance provided by pre-aggregation functions in FRBS have motivated their use in other applications requiring some kind aggregation process and where the full standard monotonicity may be not required, such us image processing and deep learning. Also, their use in real classification problems are appearing in the literature.
In this sense, the session aims at providing researchers in the field with an opportunity to present their most recent developments in applications and for discussing recent trends in this area, as well as to identify potential application problems of interest for researchers.
Scope and Topics:
- Applications of pre-aggregation functions in classification problems
- Averaging and no-averaging pre-aggregation functions in applications
- Applications of pre-aggregation functions in image processing
- Application of pre-aggregation functions in deep learning
- Real case studies
- Application of pre-classes of functions
- Applications of ordered directional monotonic functions