Special Issue on Papers Selected in ISIS & SCIS 2003

The Joint Conference of the 2nd International Conference on Soft Computing and Intelligent Systems and the 5th International Symposium on Advanced Intelligent Systems (SCIS & ISIS 2004) was held at Keio University in Yokohama, Japan, on September 21-24, 2004. Over 300 papers in various fields, for example, mathematics, urban and transport planning, entertainment, intelligent control, learning, image processing, clustering, neural networks application, evolutionary computation, system modeling, fuzzy measures, and robotics were submitted to the conference. The Program Committee required reviewers in SCIS & ISIS 2004 to select excellent papers considering publication in a special issue of the Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII). Some 45 papers were selected and some of them accepted by other reviewers will be published in a two-part special issue of SCIS & ISIS 2004. In this, the first part, 13 papers have been classified into six groups — papers 1-3 under intelligent control, paper 4 under robotics, papers 5 and 6 under neural network applications, papers 7-9 under evolutionary computation applications, paper 10 under human behavior analysis, and papers 11-13 under image processing. Remaining papers currently under review will be published in the next volume. We thank the reviewers for their time and effort in making these special issues possible so quickly, and thank the JACIII editorial board, especially Profs. Hirota and Fukuda, the Editors-in-Chief and Managing Editor Kenta Uchino for their invaluable aid and advice in putting these special issues together. This issue is dedicated to the late Prof. Toshiro Terano, who passed away on February 15, 2005. He will be greatly missed.

SCIS & ISIS is a biennial international joint conference on soft computing and intelligent systems, with research ranging from fuzzy systems, neural networks, and evolutionary computation to multi-agent systems, artificial intelligence, and robotics. SCIS & ISIS 2010 consisted of the 5th International Conference on Soft Computing and Intelligent Systems (SCIS) and the 11th International Symposium on Advanced Intelligent Systems (ISIS), held at Okayama Convention Center on December 8-12, 2010. Original presentations numbered 302 and participants 322. After preliminary selection by SCIS & ISIS 2010 session chairs, we listed over 70 papers to be published in extended form in the Special Issue of the Journal of Advanced Computational Intelligence and Intelligent Informatics. After inviting these authors to submit papers for this special issue, we had two referees to review them and accepted 27 for publication in Vol.15, Nos.7 and 8 in 2011. This special issue presents 15 of these papers covering most conference topics, including fuzzy theory, learning methods, neural networks, and evolutionary computation, with a focus on reinforcement learning, multi-agent system, nonlinear estimation, and real-world applications to visual system, robotics and energy. We thank the authors and reviewers for their invaluable contributions toward making this special issue possible. We are also grateful to Editors-in-chief Prof. Toshio Fukuda of Nagoya University and Prof. Kaoru Hirota of the Tokyo Institute of Technology for inviting us to serve as Guest Editors.

Applications of Soft Computing to Human-centered Information Systems

Intelligent Engineering Systems

Rule extraction from data is one of the key technologies for solving the bottlenecks in artificial intelligence. Artificial neural networks are well suited for representing any knowledge in given data. Extraction of logical/fuzzy rules from the trained artificial neural network is of great importance to researchers in the fields of artificial intelligence and soft computing. Fuzzy rule sets are capable of approximating any nonlinear mapping relationships. Extraction of rules from data has been discussed in terms of fuzzy modeling, fuzzy clustering, and classification with fuzzy rule sets. This special issue entitled"Rule Extraction from Data" is aimed at providing the readers with good insights into the advanced studies in the field of rule extraction from data using neural networks/fuzzy rule sets. I invited seven research papers best suited for the theme of this special issue. All the papers were reviewed rigorously by two reviewers each. The first paper proposes an interesting rule extraction method from data using neural networks. Ishikawa presents a combination of learning with an immediate critic and a structural learning with forgetting. This method is capable of generating skeletal networks for logical rule extraction from data with correct and wrong answers. The proposed method is applied to rule extraction from lense data. The second paper presents a new methodology for logical rule extraction based on transformation of MLP (multilayered perceptron) to a logical network. Duck et al. applied their C-MLP2LN to the Iris benchmark classification problem as well as real-world medical data with very good results. In the third paper, Geczy and Usui propose fuzzy rule extraction from trained artificial neural networks. The proposed algorithm is implied from their theoretical study, not from heuristics. Their study enables to initially consider derivation of crisp rules from trained artificial neural network, and in case of conflict, application of fuzzy rules. The proposed algorithm is experimentally demonstrated with the Iris benchmark classification problem. The fourth paper presents a new framework for fuzzy modeling using genetic algorithm. The authors have broken new ground of fuzzy rule extraction from neural networks. For the fuzzy modeling, they have proposed a particular type of neural networks containing nodes representing membership functions. In this fourth paper, the authors discuss input variable selection for the fuzzy modeling under multiple criteria with different importance. A target system with a strong nonlinearity is used for demonstrating the proposed method. Kasabov, et al. present, in the fifth paper, a method for extraction of fuzzy rules that have different level of abstraction depending on several modifiable thresholds. Explanation quality becomes better with higher threshold values. They apply the proposed method to the Iris benchmark classification problem and to a real world problem. J. Yen and W. Gillespie address interpretability issue of Takagi-Sugeno-Kang model, one of the most popular fuzzy mdoels, in the fifth paper. They propose a new approach of fuzzy modeling that ensures not only a high approximation of the input-output relationship in the data, but also good insights about the local behavior of the model. The proposed method is applied to fuzzy modeling of sinc function and Mackey-Glass chaotic time series data. The last paper discusses fuzzy rule extraction from numerical data for high-dimensional classification problems. H.Ishibuchi, et al. have been pioneering methods for classification of data using fuzzy rules and genetic algorithm. In this last paper, they introduced a new criterion, simplicity of each rule, together with the conventional ones, compactness of rule base and classification ability, for high-dimensional problem. The Iris data is used for demonstrating their new classification method. They applied it also to wine data and credit data. I hope that the readers will be encouraged to explore the frontier to establish a new paradigm in the field of knowledge representation and rule extraction.

The development of technology demands Computer Science graduates to have interdisciplinary knowledge and skills. Traditional curricula are less adaptive in addressing the dynamic industry needs and focus on developing soft skills. This article discusses the design of an interdisciplinary curriculum based on KKNI for Computer Science programs with a focus on soft computing and intelligent systems. This curriculum design integrates knowledge and skills from various disciplines, with an emphasis on the development of soft skills. The curriculum structure is divided into two tracks: the Mandatory Track focusing on the fundamental foundations of Computer Science and soft skills, and the Elective Track focusing on deepening interests and talents in the field of soft computing and intelligent systems, such as Artificial Neural Networks, Microcontrollers, Data Mining, Embedded Systems, Fuzzy Logic, and Computer Vision. This research adopts a Research and Development (R&D) approach with the ADDIE model. Data are collected through surveys, interviews, and document analysis. It is expected that this curriculum design will produce Computer Science graduates with comprehensive, adaptive knowledge and skills, ready for the digital era workforce, particularly in the field of soft computing and intelligent systems.

We describe in this book, new methods for building intelligent systems using type-2 fuzzy logic and soft computing techniques. Soft Computing (SC) consists of several computing paradigms, including type-1 fuzzy logic, neural networks, and genetic algorithms, which can be used to create powerful hybrid intelligent systems. In this book, we are extending the use of fuzzy logic to a higher order, which is called type-2 fuzzy logic. Combining type-2 fuzzy logic with traditional SC techniques, we can build powerful hybrid intelligent systems that can use the advantages that each technique offers. We consider in this book the use of type-2 fuzzy logic and traditional SC techniques to solve pattern recognition problems in real-world applications. We consider in particular the problems of face, fingerprint and voice recognition. We also consider the problem of recognizing a person by integrating the information given by the face, fingerprint and voice of the person. Other types of applications solved with type-2 fuzzy logic and SC techniques, include intelligent control, intelligent manufacturing, and adaptive noise cancellation.

Much work has been done recently in soft computing, reflecting the growing, widespread interest in the emerging theory and technological development in this field. Soft computing has also been the subject of a number of new scientific conferences and symposiums, including the Joint 1st International Conference on Soft Computing and Intelligent Systems (SCIS) and 3rd International Symposium on Advanced Intelligent Systems (ISIS) at the National Institute of Advanced Industrial Science and Technology at Tsukuba on October 21–25, 2002. The first and second ISIS were held in Korea, and the joint conference has become internationally recognized. The conference at Tsukuba featured over 200 papers and discussions and information exchange by over 250 participants interested in state–of–the–art soft computing. The 20 papers in this special issue were selected from 209 of these conference presentations. In the selection process, guest editors first requested recommendation of papers to session chairs and organizers. From the resulting list, we asked if they could submit their papers. Submitted papers were reviewed as usual for this journal based on the JACIII standard, resulting in the acceptance of these 20. Most papers have been rewritten and updated, and we are proud of their high quality–a reflection of the SCIS & ISIS conference review process. Papers come from different areas: two focus on theory, including modal logic. Several study fuzzy control. Still others deal with different aspects of robotics. They also cover data analysis, image analysis, knowledge analysis, and language studies involving soft computing. This issue thus provides a concise summary of state–of–the–art soft computing methodologies. We thank the referees for their untiring efforts to complete reviews within the limited time given. We also thank Professor Kaoru Hirota and Professor Toshio Fukuda, editors–in–chief of this journal, for their kind invitation to this special issue. Professor Hirota, who is also the International Advisory Board Chair, and Dr. Takanori Shibata, the General Chair, are largely responsible for the great success of the SCIS & ISIS 2003 conference, where Guest Editor Miyamoto served the Program Chair and Yasunobu served the Special Sessions Chair. The results of their work are thus reflected in this issue. In closing, we also thank the staff at Fuji Technology Press for its on going assistance.

Proceedings of Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems in Okayama on December 8-12, 2010 (SCIS & ISIS 2010)

2018 Joint 10th International Conference on Soft Computing and Intelligent Systems (SCIS) and 19th International Symposium on Advanced Intelligent Systems (ISIS)

Proceedings of Joint 3rd International Conference on Soft Computing and Intelligent Systems and 7th International Symposium on advanced Intelligent Systems in Tokyo on September 20-24, 2006 (SCIS & ISIS 2006)

2016 Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems (ISIS)

The rough set theory is a new mathematical tool to study vague and uncertain information, and is widely used in intelligent systems. In this paper, the basic ideas of rough set theory are introduced, and the notion of up and low approximation sets, attribute reduction, core and some extensions of rough set theory are also presented. Then the application of rough set theory in intelligent systems. The combination of rough sets with fuzzy sets, neural network and genetic algorithms is mainly reviewed, which gives new ideas and methods to solve the hard problems in intelligent control.

Rapid development in computer and sensor technology not only used for highly specialised applications but widespread and pervasive across a wide range of business and industry has facilitated easy capture and storage of immense amounts of data. Examples of such data collection include medical history data in health care, financial data in banking, point of sale data in retail, plant monitoring data based on instant availability of various sensor readings in various industries, or airborne hyperspectral imaging data in natural resources identification to mention only a few. However, with an increasing computer power available at affordable prices and the availability of vast amount of data there is an increasing need for robust methods and systems, which can take advantage of all available information. In essence there is a need for intelligent and smart adaptive methods but do they really exist? Are there any existing intelligent techniques which are more suitable for certain type of problems than others? How do we select those methods and can we be sure that the method of choice is the best for solving our problem? Do we need a combination of methods and if so then how to best combine them for different purposes? Are there any generic frameworks and requirements which would be highly desirable for solving data intensive and unstationary problems? All these questions and many others have been the focus of research vigorously pursued in many disciplines and some of them will be discussed in the talk and have been addressed in greater detail in our recently compiled book with the same title: ”Do Smart Adaptive Systems Exist?”. One of the more promising approaches to constructing smart adaptive systems is based on intelligent technologies including artificial neural networks, fuzzy systems, methods from machine learning, parts of learning theory and evolutionary computing which have been especially successful in applications where input-output data can be collected but the underlying physical model is unknown. The incorporation of intelligent technologies has been used in the conception and design of complex systems in which analytical and expert systems techniques are used in combination. Viewed from a much broader perspective, the above mentioned intelligent technologies are constituents of a very active research area known under the names of soft computing, computational intelligence or hybrid intelligent systems. However, hybrid soft computing frameworks are relatively young, even comparing to the individual constituent technologies, and a lot of research is required to understand their strengths and weaknesses. Nevertheless hybridization and combination of intelligent technologies within a flexible open framework seem to be the most promising direction in achieving the truly smart and adaptive systems today. Despite all the challenges it is unquestionable that smart adaptive intelligent systems and intelligent technology have started to have a huge impact on our everyday life and many applications can already be found in various commercially available products as illustrated in the recent report compiled by one of the world’s leading think tank advanced technology organisations and very suggestively titled: ”Get smart: How intelligent technology will enhance our world”.

The concept of fusion of soft computing and hard computing has rapidly gained importance over the last few years. Soft computing is known as a complementary set of techniques such as neural networks, fuzzy systems, or evolutionary computation which are able to deal with uncertainty, partial truth, and imprecision. Hard computing, i.e., the huge set of traditional techniques, is usually seen as the antipode of soft computing. Fusion of soft and hard computing techniques aims at exploiting the particular advantages of both realms. This article introduces a multi-dimensional categorization scheme for fusion techniques and applies it by analyzing several fusion techniques where the soft computing part is realized by a neural network. The categorization scheme facilitates the discussion of advantages or drawbacks of certain fusion approaches, thus supporting the development of novel fusion techniques and applications.