Special Issue on Papers Selected in ISIS & SCIS 2003

2004 ◽  
Vol 8 (4) ◽  
pp. 349-349
Author(s):  
Yong-Soo Kim ◽  
◽  
Kwee-Bo Sim ◽  
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Genetic Algorithms ◽  
Soft Computing ◽  
Intelligent Control ◽  
Intelligent Systems ◽  
Fuzzy Controller ◽  
Intelligent System ◽  
Wide Spectrum ◽  
Special Issue

This special issue of journal covers a broad field ranging from intelligent systems to robotics. These papers were selected among the papers that were presented at the Joint 4th International Symposium on Advanced Intelligent Systems and 2nd International Conference on Soft Computing and Intelligent Systems which was held in Jeju, Korea on September 25-28, 2003. In the above symposium, there was a wide spectrum of intelligent systems and related topics, including sessions: intelligent systems, intelligent control, fuzzy sets, fuzzy systems, neural networks, robotics, genetic algorithms, image processing, soft computing, artificial life, etc. Many interesting results were presented at the symposium. Among these various papers, this special issue offers a selection of sixteen papers that contribute to advances of intelligent systems in various aspects. The topics that the selected papers deal with are fuzzy controller for the mobile robot control, neural networks and their application to image processing, intelligent control for a robot, intelligent system for probe detection, fuzzy image processing, genetic algorithms, fuzzy clustering for incomplete categorical data, predictive fuzzy controller for an electric four-wheeled vehicle. As guest editors of this special issue, we would like to express our thanks to authors for their contribution, the anonymous referees for their review, and Prof. Kaoru Hirota for his giving the opportunity to publish this special issue.

Advanced Computational Intelligence in Control Theory and Applications

1999 ◽  
Vol 3 (2) ◽  
pp. 67-67
Author(s):  
Kazuo Tanaka ◽  
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Control Theory ◽  
Soft Computing ◽  
Computational Intelligence ◽  
Intelligent Systems ◽  
Special Issue ◽  
Systems And Control ◽  
And Control

We are witnessing a rapidly growing interest in the field of advanced computational intelligence, a "soft computing" technique. As Prof. Zadeh has stated, soft computing integrates fuzzy logic, neural networks, evolutionary computation, and chaos. Soft computing is the most important technology available for designing intelligent systems and control. The difficulties of fuzzy logic involve acquiring knowledge from experts and finding knowledge for unknown tasks. This is related to design problems in constructing fuzzy rules. Neural networks and genetic algorithms are attracting attention for their potential in raising the efficiency of knowledge finding and acquisition. Combining the technologies of fuzzy logic and neural networks and genetic algorithms, i.e., soft computing techniques will have a tremendous impact on the fields of intelligent systems and control design. To explain the apparent success of soft computing, we must determine the basic capabilities of different soft computing frameworks. Give the great amount of research being done in these fields, this issue addresses fundamental capabilities. This special issue is devoted to advancing computational intelligence in control theory and applications. It contains nine excellent papers dealing with advanced computational intelligence in control theory and applications such as fuzzy control and stability, mobile robot control, neural networks, gymnastic bar action, petroleum plant control, genetic programming, Petri net, and modeling and prediction of complex systems. As editor of this special issue, I believe that the excellent research results it contains provide the basis for leadership in coming research on advanced computational intelligence in control theory and applications.

Selected Papers from SCIS & ISIS 2006 – No.1

2007 ◽  
Vol 11 (6) ◽  
pp. 535-535
Author(s):  
Naoyuki Kubota ◽  
Keyword(s):  
Neural Networks ◽  
Soft Computing ◽  
Computational Intelligence ◽  
Intelligent Systems ◽  
Manufacturing Systems ◽  
Current Issue ◽  
Multi Agent Systems ◽  
Special Issue ◽  
Tokyo Institute ◽  
Institute Of Technology

SCIS & ISIS is a biennial international joint conference in the field of soft computing and intelligent systems, including branches of researches from fuzzy systems, neural networks, evolutionary computation, multi-agent systems, artificial intelligence or robotics. SCIS & ISIS 2006 falls on the 3rd International Conference on Soft Computing and Intelligent Systems (SCIS) and the 7th International Symposium on Advanced Intelligent Systems (ISIS) held at Tokyo Institute of Technology, in Tokyo, Japan, on September 20-24, 2006. In this conference, 464 original papers were accepted for presentation and the number of attendees was 526. After preliminary selection and review made by the session chairs and the International Program Committees of SCIS & ISIS 2006, we have selected more than 50 papers to be published in extended form in the Special Issue of the Journal of Advanced Computational Intelligence and Intelligent Informatics. The accepted papers are published as the special issues in Vol.11, No.6, 7, and 8 in 2007. This current issue presents 23 papers and covers most of the topics of the conference including fuzzy theories, self-organizing maps, and the optimization of neural networks. The learning and search methods in computational intelligence and real-world applications to image processing, robotics and manufacturing systems are highlighted in this current issue. I would like to thank all the authors and reviewers for their contribution to make this special issue possible. I am also grateful to Prof. Toshio Fukuda, Nagoya University and Prof. Kaoru Hirota, Tokyo Institute of Technology, Editors-in-chief, for inviting me to serve as Guest Editor of this Journal.

scholarly journals Special Issue on Intelligent Technologies, Fuzzy Systems and Applications

2003 ◽  
Vol 7 (3) ◽  
pp. 251-251
Author(s):  
Nguyen Hoang Phuong ◽  
Keyword(s):  
Data Mining ◽  
Genetic Algorithms ◽  
Soft Computing ◽  
Fuzzy Systems ◽  
Wide Spectrum ◽  
Support Vector ◽  
Equivalent Transformation ◽  
Self Organizing Map ◽  
Special Issue

In this issue, we are featuring fifteen papers devoted to intelligent technologies, fuzzy systems and their applications as a special issue of the journal. The papers are selected from papers accepted and presented at the joint Third International Conference on Intelligent Technologies and Third Vietnam-Japan Symposium on Fuzzy Systems and Applications (InTech/VJFuzzy'2002) that was held in Hanoi, Vietnam on December 3-5, 2002. In InTech/VJFuzzy'2002, there was a wide spectrum of research topics on artificial intelligence, fuzzy systems, soft computing, and related fields such as"fuzzy logic", "fuzzy set theory", "fuzzy systems", "AI techniques", "Bayesian networks", "genetic algorithms", "neural networks", "knowledge discovery and data mining", "speech recognition", "soft computing in medicine", among others. More than 60 papers were accepted and presented by authors from many countries, including Australia, China, India, Korea, Germany, France, Thailand, Taiwan, Japan, Vietnam, and U.S.A. Fifteen papers that received outstanding recommendations from its reviews were selected in this special issue. The topics addressed by these selected papers include fuzzy rule systems, fuzzy inference methods, fuzzy and rough models, problem solving with equivalent transformation, genetic algorithms, reinforcement learning, non–monotonic reasoning, support vector machines, Hedge algebra, intelligent control, natural language understanding, self–organizing map learning, soft computing and data mining in medicine. As editors of this special issue, we would like to express our sincere gratitude to paper's authors in this issue. Our special thanks go to the anonymous referees for their excellent job, Ms. Kumiko Sato for her help in coordinating the publication of the issue, the Editorial Board of JACIII, especially Professor Kaoru Hirota for his great support and encouragement. Finally, we wish to thank Professors Hung T. Nguyen, Michio Sugeno and Pratit Santiprabhob for their help and contribution to InTech/VJFuzzy'2002. Without their support, the InTech/VJFuzzy'2002 and this issue would not be possible.

Selected Papers from SCIS & ISIS 2010 – No.1

2011 ◽  
Vol 15 (7) ◽  
pp. 813-813
Author(s):  
Keigo Watanabe ◽  
Kazuhiro Ohkura ◽  
Kiyotaka Izumi
Keyword(s):  
Neural Networks ◽  
Evolutionary Computation ◽  
Soft Computing ◽  
Intelligent Systems ◽  
Fuzzy Theory ◽  
Multi Agent Systems ◽  
Special Issue ◽  
Convention Center ◽  
Multi Agent ◽  
Institute Of Technology

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.

Special Issue on Selected Papers in SCIS & ISIS 2004 – No.2

2005 ◽  
Vol 9 (3) ◽  
pp. 225-225
Author(s):  
Takehisa Onisawa ◽  
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Visual Information ◽  
Intelligent Systems ◽  
System Modeling ◽  
Special Issue ◽  
Gene Expressions ◽  
Floor Plans ◽  
System Verification

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) held at Keio University in Yokohama, Japan, on September 21-24, 2004, attracted over 300 papers in fields such as mathematics, urban and transport planning, entertainment, intelligent control, learning, image processing, clustering, neural networks applications, evolutionary computation, system modeling, fuzzy measures, and robotics. The Program Committee requested reviewers in SCIS & ISIS 2004 to select papers for a special issue of the Journal of Advanced Computational Intelligence & Intelligent Informatics (JACIII), with 27 papers accepted for publication in a two-part SCIS & ISIS 2004 special – Vol.9, No.2, containing 13 and the second part containing 14. Paper 1 details tap-changer control using neural networks. Papers 2-5 deal with image processing and recognition – Paper 2 proposing a model of saliency-driven scene learning and recognition and applying its model to robotics, paper 3 discussing breast cancer recognition using evolutionary algorithms, paper 4 covering a revised GMDH-typed neural network model applied to medical image recognition, paper 5 presenting how to compensate for missing information in the acquisition of visual information applied to autonomous soccer robot control. Paper 6 details gene expressions networks for 4 fruit fly development stages. Paper 7 proposes an α-constrained particle swarm optimized for solving constrained optimization problem. Paper 8 develops a fuzzy-neuro multilayer perceptron using genetic algorithms for recognizing odor mixtures. Paper 9 discusses how to integrate symbols into neural networks for the fusion of computational and symbolic processing and its effectiveness demonstrated through simulations. Paper 10 proposes an electric dictionary using a set of nodes and links whose usefulness is verified in experiments. Paper 11 presents a multi-agent algorithm for a class scheduling problem, showing its feasibility through computer simulation. Paper 12 proposes inductive temporal formula specification in system verification, reducing memory and time in the task of system verification. Paper 13 applies an agent-based approach to modeling transport using inductive learning by travelers and an evolutionary approach. The last paper analyzes architectural floor plans using a proposed index classifying floor plans from the user's point of view. We thank reviewers for their time and effort in making these special issues available so quickly, and thank the JACIII editorial board, especially Editor-in-Chief Profs. Hirota and Fukuda and Managing Editor Kenta Uchino, for their invaluable aid and advice in putting these special issues together.

Special Issue on Selected Papers in SCIS & ISIS 2004 - No.1

2005 ◽  
Vol 9 (2) ◽  
pp. 91-91
Author(s):  
Takehisa Onisawa ◽  
Keyword(s):  
Image Processing ◽  
Evolutionary Computation ◽  
Intelligent Control ◽  
Intelligent Systems ◽  
System Modeling ◽  
Program Committee ◽  
Special Issue ◽  
Network Applications ◽  
Control Learning

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.

scholarly journals Fuzzy Logic for Intelligent Control System Using Soft Computing Applications

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2617
Author(s):  
Catalin Dumitrescu ◽  
Petrica Ciotirnae ◽  
Constantin Vizitiu
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Soft Computing ◽  
Intelligent Control ◽  
Fuzzy Controller ◽  
Control Technique ◽  
Distributed Intelligence ◽  
High Level ◽  
The Impact ◽  
Fuzzy Subsets

When considering the concept of distributed intelligent control, three types of components can be defined: (i) fuzzy sensors which provide a representation of measurements as fuzzy subsets, (ii) fuzzy actuators which can operate in the real world based on the fuzzy subsets they receive, and, (iii) the fuzzy components of the inference. As a result, these elements generate new fuzzy subsets from the fuzzy elements that were previously used. The purpose of this article is to define the elements of an interoperable technology Fuzzy Applied Cell Control-soft computing language for the development of fuzzy components with distributed intelligence implemented on the DSP target. The cells in the network are configured using the operations of symbolic fusion, symbolic inference and fuzzy–real symbolic transformation, which are based on the concepts of fuzzy meaning and fuzzy description. The two applications presented in the article, Agent-based modeling and fuzzy logic for simulating pedestrian crowds in panic decision-making situations and Fuzzy controller for mobile robot, are both timely. The increasing occurrence of panic moments during mass events prompted the investigation of the impact of panic on crowd dynamics and the simulation of pedestrian flows in panic situations. Based on the research presented in the article, we propose a Fuzzy controller-based system for determining pedestrian flows and calculating the shortest evacuation distance in panic situations. Fuzzy logic, one of the representation techniques in artificial intelligence, is a well-known method in soft computing that allows the treatment of strong constraints caused by the inaccuracy of the data obtained from the robot’s sensors. Based on this motivation, the second application proposed in the article creates an intelligent control technique based on Fuzzy Logic Control (FLC), a feature of intelligent control systems that can be used as an alternative to traditional control techniques for mobile robots. This method allows you to simulate the experience of a human expert. The benefits of using a network of fuzzy components are not limited to those provided distributed systems. Fuzzy cells are simple to configure while also providing high-level functions such as mergers and decision-making processes.

Prediction of International Stock Markets Based on Hybrid Intelligent Systems

2016 ◽  
pp. 110-124
Author(s):  
Salim Lahmiri
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Stock Market ◽  
Intelligent Systems ◽  
Fuzzy Inference ◽  
Intelligent System ◽  
Market Price ◽  
Hybrid Intelligent Systems ◽  
Out Of Sample ◽  
Artificial Neural Network Ann

This paper compares the accuracy of three hybrid intelligent systems in forecasting ten international stock market indices; namely the CAC40, DAX, FTSE, Hang Seng, KOSPI, NASDAQ, NIKKEI, S&P500, Taiwan stock market price index, and the Canadian TSE. In particular, genetic algorithms (GA) are used to optimize the topology and parameters of the adaptive time delay neural networks (ATNN) and the time delay neural networks (TDNN). The third intelligent system is the adaptive neuro-fuzzy inference system (ANFIS) that basically integrates fuzzy logic into the artificial neural network (ANN) to better model information and explain decision making process. Based on out-of-sample simulation results, it was found that contrary to the literature GA-TDNN significantly outperforms GA-ATDNN. In addition, ANFIS was found to be more effective in forecasting CAC40, FTSE, Hang Seng, NIKKEI, Taiwan, and TSE price level. In contrary, GA-TDNN and GA-ATDNN were found to be superior to ANFIS in predicting DAX, KOSPI, and NASDAQ future prices.

Voltage Instability Detection Using Neural Networks

2011 ◽  
pp. 1596-1602
Author(s):  
Adnan Khashman ◽  
Kadri Buruncuk ◽  
Samir Jabr
Keyword(s):  
Neural Networks ◽  
Power Systems ◽  
Power Distribution ◽  
Distribution System ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Detection System ◽  
Nonlinear Problems ◽  
Voltage Instability ◽  
Voltage Output

The explosive growth in decision-support systems over the past 30 years has yielded numerous “intelligent” systems that have often produced less-than-stellar results (Michalewicz Z. et al., 2005). The increasing trend in developing intelligent systems based on neural networks is attributed to their capability of learning nonlinear problems offline with selective training, which can lead to sufficiently accurate online response. Artificial neural networks have been used to solve many problems obtaining outstanding results in various application areas such as power systems. Power systems applications can benefit from such intelligent systems; particularly for voltage stabilization, where voltage instability in power distribution systems could lead to voltage collapse and thus power blackouts. This article presents an intelligent system which detects voltage instability and classifies voltage output of an assumed power distribution system (PDS) as: stable, unstable or overload. The novelty of our work is the use of voltage output images as the input patterns to the neural network for training and generalizing purposes, thus providing a faster instability detection system that simulates a trained operator controlling and monitoring the 3-phase voltage output of the simulated PDS.

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