bluuebunny/crossref_metadata_2025 huggingface dataset:
An Efficient Neural Network Model for Path Planning of Car-like Robots in Dynamic Environment
Authors: Yang Simon X. ,   Advanced Robotics & Intelligent Systems (ARIS) Group School of Engineering, University of Guelph Guelph, Ontario, Canada, NlG 2W1, Meng Max ,   Advanced Robotics and Teleoperation (ART) Lab Department of Electrical and Computer Engineering, University of Alberta Edmonton, Alberta, Canada, T6G 2G7
Published: April 2016

In this paper, an effcient neural network approach to real-time path planning with obstacle avoidance of holonomic car-like robots in a dynamic environment is proposed. The dynamics of each neuron in this biologically inspired, topologically organized neural network is characterized by a shunting equation or an additive equation. The state space of the neural network is the configuration space of the robot. There are only local lateral connections among neurons. Thus the computational complexity linearly depends on the neural network size. The real-time collision-free path is planned through the dynamic neural activity landscape of the neural network without explicitly searching over neither the free workspace nor the collision paths, without any prior knowledge of the dynamic environment, without any learning procedures, and without any local collision checking procedures at each step of the robot movement. Therefore it is computationally efficient. The stability of the neural network is proven by both qualitative analysis and the Lyapunov stability theory. The effectiveness and efficiency are demonstrated through simulation studies.
System Architecture for Support of Knowledge Management
Authors: Paralic Marek ,   Department of Computers and Informatics, University of Technology, Kosice 04120 Kosice, Letná 9, Slovakia, Sabol Tomás , Mach Marian ,   Department of Cybernetics and Artificial Intelligence, University of Technology, Kosice 041 20 Kosice, Letná 9, Slovakia
Published: April 2016

In this paper, KnowWeb, a European Commission funded research project, is briefly introduced. The project focuses on the storage of knowledge in corporate organizational memory and retrieval of relevant knowledge chunks from memory. Retrieval is based on company-specific conceptual terms instead of traditional keywords. The employed knowledge-based approach enables searching not only in physical document space but also in document context space. Flexible architecture that can be adopted by different SMEs with specific distributed environments in a simple and straightforward way is a necessary condition for success of such a system. Therefore, in addition to the knowledge modeling approach, the distributed architecture of our proposed system is described.
Cluster Analysis as a First Step in the Knowledge Discovery Process
Authors: Rauber Andreas ,   Department of Software Technology, Vienna University of Technology Favoritenstr., 9 - 11 / 188, A - 1040 Vienna, Austria, Paralic Jan ,   Department of Cybernetics and Artificial Intelligence, Technical University of Kosice, Letna 9, 04200 Kosice, Slovak Republic
Published: April 2016

Cluster analysis is one of the most prominent methods for the analysis of large, unknown datasets. It provides a particularly suitable tool for obtaining a first overview of data, forming a prominent starting point for further evaluation. . In this paper, we present some lessons learned during the application of two clustering approaches to the analysis of castle admission ticket sales data. A Bayesian unsupervised classification based on AutoClass and an unsupervised neural network, the Self-Organizing Map, are used to obtain a first impression of the available data to form the basis for further exploration. We show that this type of cluster analysis provides a suitable first step in the knowledge discovery process. The different types of result representation and their suitability of providing a first insight into datasets are analyzed and compared.
Trajectory Tracking Control of Unconstrained Object Using the SIRMs Dynamically Connected Fuzzy Inference Model
Authors: Yi Jianqiang ,   Technology Research Center, Mycom, Inc. 12, S. Shimobano, Saga Hirosawa, Ukyo, Kyoto 616-8303, Japan, Yubazaki Naoyoshi , Hirota Kaoru ,   Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, 4259 Nagatsuta, Midori, Yokohama 226-8502, Japan
Published: April 2016

A trajectory tracking experiment system taking an unconstrained table-tennis ball as the control object is constructed, and a fuzzy controller based on the SIRMs dynamically connected fuzzy inference model is proposed. For each of the three input items of the fuzzy controller, a SIRM (Single Input Rule Module) is established and an importance degree is defined. Especially for the input item corresponding to ball velocity, its importance degree is tuned dynamically according to moving conditions. The summation of the products of the importance degree and the fuzzy inference result of the SIRMs is calculated to control the angles of a table, making the ball on the table move along a desired trajectory. A virtual spiral asymptotic trajectory is also introduced to give the object an adequate desired position at each sampling time. Tracking experiment results for three kinds of circles and one kind of ellipses show that in more than 80% of the experiments performed under the SIRMs dynamically connected fuzzy inference model, the maximum tracking error is smaller than 0.05m and the unevenness of the sampling steps necessary for each round is very small. Compared with conventional fuzzy controller, the SIRMs dynamically connected fuzzy inference model is proved to be effective in tracking control of unconstrained objects.
A Fuzzy Rule Extraction Method for ANFIS Using CFCM and Fuzzy Equalization
Authors: Chun Myung-Geun ,   School of Electrical and Electronic Engineering Chungbuk National University Gaesin-Dong Cheong-Ju Chungbuk 361-763 KOREA, Kwak Keun-Chang , Ryu Jeong-Woong , Pedrycz Witold ,   Department of Electrical and Computer Engineering University of Alberta, Edmonton, Canada
Published: April 2016

In this paper, an efficient fuzzy rule generation scheme for Adaptive Network-based Fuzzy Inference System (ANFIS) using the conditional fuzzy c-means (CFCM) and fuzzy equalization (FE) methods is proposed. Here, the CFCM is adopted to render clusters, which can represent the homogeneous properties of the given input and output fuzzy data. And also the FE method is used to automatically construct the fuzzy membership functions for ANFIS. From this, we can systematically obtain a small size of fuzzy rules that shows satisfactory performance for the given problems. We applied the proposed method to the truck-backing control and Box-Jenkins modeling problems and obtained a better result than previous work.
Design of Diagnosis System for Insulation Degradation by Using Neurofuzzy Model
Authors: Kim Yigon ,   Department of Electrical and Semiconductor Engineering, Yosu National. University, Korea, Jung Yang Hee , Bae Yong Chul
Published: April 2016

Insulation aging diagnosis provides early warning of electrical equipment defects that helps avoid loss from unexpected production line shutdown. Since relations of insulation aging and partial discharge dynamics are nonlinear, it is very difficult to provide early warning in electrical equipment. This paper suggests a new method for diagnosing insulation aging that measures partial discharge on-line from DAS(Data Acquisition System) and acquires 2D patterns from analyzing it using wavelets. Using this data, design of a neurofuzzy model that diagnoses electrical equipment is investigated. Validity of the new method is confirmed by numerical simulation.
Support System for Multimedia Information Data Acquisition Based on Fuzzy Inference with a Fuzzy Shift
Authors: Oh Kabsuk ,   Department of Computer Science, TongMyong College,, Hirota Kaoru ,   Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan.
Published: April 2016

Support system construction for multimedia information data acquisition based on fuzzy inference with a concept of fuzzy shift is proposed, where the multimedia means the five senses. Observed information from the outside world is characterized by VAGOT (visual, acoustic, gustatory, olfactory, and tactile) time series data. Here, multimedia information centers on image and sound are represented by membership functions. Fuzzy rules based on visual and acoustic information are used to identify the appropriate time interval on multimedia input data. The proposed system is constructed by rule construction, data acquisition, a rule base, and a support system. When an instruction of the user has temporal vagueness, rule base is updated by the support system based on fuzzy shift. The method is applied to an experiment related to the decision of a temporal boundary subset of vehicles and results verify the feasibility of the proposed method.
Fuzzy Control of Back-Propagation Training
Authors: Negnevitsky Michael ,   School of Engineering, University of Tasmania,, Ringrose Martin J.
Published: April 2016

A fuzzy logic controller for updating training parameters in the error back-propagation algorithm is presented. The controller is based on heuristic rules for speeding up the convergence of training process, incorporating both learning rate and momentum constant changes.
Indexing Visual Features Using a Hybrid Neural Network
Authors: Jin Jesse S. ,   Department of Computer Science, University of Sydney NSW 2006, Australia, Wang Henry C. , Gedeon Tom ,   School of Information Technology, Murdoch University WA, Australia
Published: April 2016

Indexing and retrieving visual information is an important issue in multimedia development. It involves handling high dimensional vectors. Current tree-based high dimensional index structures, such as R-tree, SS+-tree, TV-tree, etc, have the similar low bound to the one-dimensional comparison-based search methods. It is far from being practical in the multimedia area. We propose a fast indexing method using surrogate coding. It possesses many good properties such as preserving similarity ranking and being fast in retrieval. It also preserves a clustered space and is easy to maintain.
Implementation of Fuzzy Legal Expert System FLES
Authors: Hirota Kaoru ,   Tokyo Institute of Technology, Yokohama, Xu MingQiang , Takama Yasufumi , Yoshino Hajime ,   Lucent Technologies, Tokyo,   Meiji Gakuin University, Tokyo
Published: April 2016

A fuzzy legal expert system FLES based on a fuzzy Housdorff similarity measure is implemented. The reasoning approach in this system includes the fuzzy case-based reasoning that is composed of knowledge representation, retrieval, and inference. The proposed approaches are illustrated by the experiments, where the target law is CISG (United Nation Convention on Contract for the International Sale of Goods).
Robotic System based on Computational Intelligence - Evolutionary Generation of Regrasping Motion
Authors: Fukuda Toshio ,   Center for Cooperative Research in Advanced Science & Technology Nagoya University, Furo-cho 1, Chikusa-ku, Nagoya, 464-8603, Japan, Hasegawa Yasuhisa ,   Department of Micro System Engineering Nagoya University, Furo-cho 1, Chikusa-ku, Nagoya, 464-8603, Japan
Published: April 2016

The control of multifingered robot hands has been the subject of recent interest. To regrasp an object, there are many parameters to be determined; grasping points, grasping force, regrasping phases, finger allocation and so on. It is difficult to optimize such manipulation parameters for achieving effective manipulation. In this section, we propose generation of regrasping motion for a four-fingered robot hand using Evolutionary Programming (EP). Evolutionary optimization is generally able to find optimal solutions without supervisor after much iteration, which makes it almost impractical to apply a real robot directly. Therefore, we apply the controller in numerical simulation to the real robot hand. We show effectiveness of the propose method for the regrasping motion with experimental results.
Deep Fusion of Computational and Symbolic Intelligent Processing by Symbol Emergence
Authors: Tano Shun’ichi ,   Graduate School of Information Systems, University of Electro-Communications, 1-5-1 Chofugaoka, Chofu-shi, Tokyo 182-8585, Japan
Published: April 2016

Through the development of the intelligent user interface system called THE RVI-desk, we found that it was indispensable to combine symbolic and computational processing in order to realize a software architecture for the next-generation intelligent user interface. Several early works toward the combination of symbolic and computational processing were briefly reviewed but the level of fusion is not deep, high and wide enough. Then two simple models, Model-A and Model-B, are described as the first step toward fusing computational and symbolic processing. Model-B is an extension of Model-A. Each of them consists of two layers. The lower layer is a neural network in which Q-learning is simulated. The inputs are state variables, and the outputs are Q-values for each action. It is tuned to update and store Q-table. The upper layer watches the activity in the lower layer to identify the group of nodes that are activated when some action in the lower layer obtains a high reward from the environment. In this way, new symbols emerge that are embedded in the lower layer to speed up learning. Model-B is extended to learn more complex concepts quickly. When an important concept is learned, the corresponding symbol is generalized and embedded in a different place at a lower level. Simulation demonstrated that symbol emergence and the forced application of these symbols in Q-learning greatly improves the performance of players playing a simple football game. This approach is a first step toward "deep fusion of computational and symbolic processing".
Evolutionary Nonlinear Multimodel Partitioning Filters
Authors: Befigiannis G. N. ,   Department of Computer Engineering and Informatics University of Patras, Rio, 26500, Patras, Greece, Demiris E. N. , Likothanassis S. D. ,   Computer Technology Institute (C.T.I.) Kolokotroni 3, 26221, Patras, Greece,   University of Patras Artificial Intelligence Research Center (U.P.A.I.R.C.) University of Patras, Rio, 26500 Patras, Greece
Published: April 2016

The problem of designing adaptive filters for nonlinear systems is faced in this work. The proposed evolution program combines the effectiveness of multimodel adaptive filters and the robustness of genetic algorithms (GAs). Specifically, a bank of different extended Kalman filters is implemented. Then, the a posteriori probability that a specific model of the bank of conditional models is the true one can be used as a GA fitness function. The superiority of the algorithm is that it evolves concurrently the models’ population with initial conditions. Thus, this procedure alleviates extended Kalman filter sensitivity in initial conditions, by estimating the best values. In addition to this, adaptive implementation is proposed that relieves the disadvantage of time-consuming GA implementation. Finally, a variety of defined crossover and mutation operators is investigated in order to accelerate the algorithm’s convergence.
Inference Algorithm Independent SVD Fuzzy Rule Base Complexity Reduction
Authors: Baranyi Péter ,   Research Group for Mechanics of the Hungarian Academy of Science, Yamb Yeung , Yang Chi Tin , Várlakic Péter , Michelberger Pél ,   Dept. Telecommunication and Telematics, Budapest University of Technology and Economics Budapest, Sztoczek u.2., Budapest, H-1111, Hungary,   Dept. Mechanical and Automation Engineering, Chinese University of Hong Kong Shatin, N.T. Hong Kong
Published: April 2016

This paper is motivated by the fact that fuzzy technique is popular engineering tool, however, its use is restricted by its exponential complexity. One of the complexity reduction techniques is the SVD based reduction method which deals with arbitrary shaped fuzzy sets. Despite its advantages it is applicable to rule bases only which are based on product-sum-gravity (PSG) inference algorithm. This paper introduces the extension of the PSG based SVD-reduction method to arbitrary inference based fuzzy algorithms. An example with the control of an automatic guided system is presented to demonstrate the reduction process.
Information Processing Based on Mixed - Classical and Fuzzy - Data Models
Authors: Takács Orsolya ,   Dept. of Measurement and Information Systems, Várkonyi-Kóczy Annamária R.
Published: April 2016

The model used to represent information during information processing could affect achievable accuracy and could determine the usability of different calculation methods. The data model must also be able to represent uncertainty and inaccuracy both of input data and results. The two most popular data models for representation of uncertain data is the "classical", probability based, and the recently introduced fuzzy data models. Both data models have their own calculation and data processing methods, but with the increasing complexity of calculation problems, a method for the mixed use of these data models is be needed. This paper deals with possible solutions for information processing based on mixed data models and examines the different conversion methods between fuzzy and probability theory based data models.
Fuzzy Control Using Piecewise Linear Membership Functions Based on Knowledge of Tuning a PID Controller
Authors: Hayashi Kenichiro ,   Applied Electronics Division, Industrial Technology Center of Saga, 114 Yaemizo, Nabeshima-cho, Saga-shi 849-0932, Japan, Otsubo Akifumi , Shiranita Kazuhiko
Published: April 2016

Tuning control parameters of a fuzzy controller depends on trial-and-error. How this can be accomplished efficiently is an important subject in fuzzy control that should be investigated. We propose a method in which membership functions of a fuzzy controller can be simply set up using the knowledge of tuning parameters in a conventional PID controller. In this method, fuzzy control is realized as follows: Piecewise linear membership functions determined using the knowledge of tuning parameters in a PID controller are set up in antecedent parts of four fuzzy control rules having simple structures. Then the simplified inference method that enables high-speed inference is applied to fuzzy control rules.
Fuzzification of Kolmogorov Theorem
Authors: López-Gótnez Angel ,   Interdisciplinary Grad. School of Science and Eng., Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ward, Yokohama 226-8502, Japan, Hirota Kaoru
Published: April 2016

The extension of the Kolmogorov’s theorem (1957) about the representation of continuous real extended functions with more than one variables as superpositions of extended functions of one variable is presented. The graphical analysis of its behavior with a function of two variables is showed. The representation of the theorem using a practical neural network is obtained.
Application of Fuzzy Set Theory and DEA Model to Evaluating Production Efficiency for Taipei City Bus Company
Authors: Tzeng Gwo-Hshiung ,   Energy and Environmental Research Group, Institute of Technology Management and Institute of Traffic and Transportation, College of Management, National Chiao Tung University, 1001, Ta-Hsuch Rd., Hsinchu 300, Taiwan, Feng Cheng-Min , Kang Chao-Chung
Published: April 2016

The purpose of this paper is to present a performance evaluation model for forecasting production efficiency for Decision Making Units (DMUs). This model is based on the fuzzy set theory, fuzzy regression, and the DEA model. A stochastic DEA approach has been proposed and used widely to analyze the performance of the uncertain input or output data, but this approach requires large data samples and assumes probability distribution in measurement error terms. The concept of fuzzy numbers was seldom considered, although the stochastic DEA approach can be used for prediction. This paper integrates fuzzy regression and fuzzy DEA as one model. The results of this research show that the model developed in this paper is applicable to evaluate the "reform policy for passenger loading operations" currently undertaken by the Taipei City Bus Company. Based on this study, the integration of fuzzy numbers, fuzzy regression, and the DEA model can be applied to evaluate production efficiency of the city bus company for the short-term future.
Chu Spaces: Towards New Foundations for Fuzzy Logic and Fuzzy Control, with Applications to Information Flow on the World Wide Web
Authors: Nguyen Nhu ,   Department of Mathematical Sciences New Mexico State University Las Cruces, NM 88003, USA, Nguyen Hung T. , Wu Berlin , Kreinovich Vladik ,   Department of Mathematical Sciences National Chengchi University Taipei, Taiwan,   Department of Computer Science University of Texas El Paso, El Paso, TX 79968, USA,   Department of Basic Science Nanjing University of Chemical Technology Nanjing 210009, P.R. China
Published: April 2016

We show that Chu spaces, a new formalism used to describe parallelism and information flow, provide uniform explanations for different choices of fuzzy methodology, such as choices of fuzzy logical operations, of membership functions, of defuzzification, etc.
Evolving Neurofuzzy System by Hybrid Soft Computing Approaches for System Identification
Authors: Kawaji Shigeyasu ,   Department of System Engineering and Information Science Graduate School of Science and Technology, Kumamoto University, 2-39-1 Kurokami, Kumamoto 860-8555 Japan, Chen Yuehui
Published: April 2016

This paper studies optimizing neurofuzzy system using a hybrid approach of a modified probabilistic incremental program evolution algorithm (MPIPE), random search algorithm, and evolutionary programming (EP). Neurofuzzy system is a combination of fuzzy system and neural network. The performance of a neurofuzzy system depends largely on selection of fuzzy membership functions, partition of input space and fuzzy rules. Two neurofuzzy models, additive and direct, are proposed in which neurofuzzy system calculation is based on tree structural representation. Without prior knowledge of the plant, parameters of fuzzy membership functions, the number of fuzzy rules and weights of neurofuzzy system are optimized using a hybrid method of MPIPE and EP algorithms simultaneously. Simulation results for identification of nonlinear systems show the feasibility and effectiveness of the proposed method.
Implementing Discrete-time Fractional-order Controllers
Authors: Machado J. A. Tenreiro ,   Dept. of Electrotechnical Engineering, Institute of Engineering of Porto Rua Dr. Antonio Bernardino de Almeida, 4200-072 Porto, Portugal
Published: April 2016

The theory of fractional calculus goes back to the beginning of the theory of differential calculus but its inherent complexity postponed the application of the associated concepts. In the last decade the progress in the areas of chaos and fractals revealed subtle relationships with the fractional calculus leading to an increasing interest in the development of the new paradigm. In the area of automatic control preliminary work has already been carried out but the proposed algorithms are restricted to the frequency domain. The paper discusses the design of fractional-order discrete-time controllers. The algorithms studied adopt the time domain, which makes them suited for z-transform analysis and discretetime implementation.
Results of Bias-variance Tests on Multi-layer Perceptron Neural Networks
Authors: Nortje Wimpie D. ,   Department of Electrical, Electronic and Computer Engineering University of Pretoria Pretoria South Africa, Holm Johann E. W. , Hancke Gerhard P. , Rudas Imre. J. , Horvath Laszlo. ,   ,,   Budapest Polytechnic, Budapest, Npsznhz u. 8, Budapest, H-1081 Hungary
Published: April 2016

Training neural networks involves selection of a set of network parameters, or weights, on account of fitting a non-linear model to data. Due to the bias in the training data and small computational errors, the neural networks’ opinions are biased. Some improvement is possible when multiple networks are used to do the classification. This approach is similar to taking the average of a number of biased opinions in order to remove some of the bias that resulted from training. Bayesian networks are effective in removing some of the bias associated with training, but Bayesian techniques are tedious in terms of computational time. It is for this reason that alternatives to Bayesian networks are investigated.
Application of Cooperative Control to Petroleum Plants Using Fuzzy Supervisory Control and Model Predictive Multi-variable Control
Authors: Kobayashi Takahiro ,   System Technology Development Center, Idemitsu Kosan Co., Ltd., Japan 26 Anesaki-kaigan, Ichihara, Chiba, 299-0107 Japan, Tani Tetsuji
Published: April 2016

This paper describes hierarchical control with fuzzy supervisory control and model predictive multivariable control (MPC) in a petroleum plant. MPC is effective in time delay, interference, and handling constraints. Fuzzy logic controllers are effective for plants with large time delay and non-linearity. Our proposed hierarchical control combines their advantages. Fuzzy supervisory control, which determines set points for MPC, consists of an estimation block and a compensation block. We use a statistical model with multi-regression analysis for the estimation block to estimate parameters of plant operation, and fuzzy logic for the compensation block to correct output of the statistical model. Hierarchical control has been applied to an actual plant in an oil refinery, and showed satisfactory performance.
Selected Papers VJFUZZY'2001
Authors: Nguyen Hoang Phuong*and Koichi Yamada**  ,   * National Center for National Science & Technology Hoang Quoc Vet Rd., Cau Giay, Hanoi, Vietnam** Department of Management & Information Systems Science Nagaoka University of Technology1603-1, Kami-Tomioka, Nagaoka, 940-2188, Japan
Published: April 2016

This special issue of the journal is devoted to Fuzzy Systems and their Applications. Today's fuzzy technology is like a mighty dragon: every year, every day, new applications appear that are more and more helpful in our peaceful life. One of the main objectives of the Second International Vietnam-Japan Symposium on Fuzzy Systems and Applications (VJFUZZY'2001) is to help this powerful technology to achieve even more. VJFUZZY'2001 was held in Hanoi, Vietnam on December 7-8, 2001. In VJFUZZY'2001, there was a wide spectrum of fuzzy research and applications, including sessions: "Fuzzy Mathematics", "Fuzzy Image/Signal Processing and Pattern recognition", "Fuzzy Optimization/Programming", "Fuzzy Data Analysis and its applications", "Towards Combining Fuzzy, probability and other techniques", "Learning, Neural Networks and Applications", "Fuzzy/Intelligent Control", "Natural Language Processing" etc. Many very interesting results were presented at the symposium. Among these various and excellent papers, this special issue offers a selection of nine papers that contribute to advances of computational intelligence in various aspects. The topics that the selected papers deal with are fuzzy relation with threshold, nonlinear optimization with convex polyhedral objective function, reinforcement learning of fuzzy control rules, learning of probabilistic causal models, data querying in fuzzy relational database, case-based reasoning in medial diagnosis, analysis of human brain activities for fuzzy and crisp calculations, fuzzy robust control of a decentralized system and image processing using fuzzy mathematical morphology. As editors of this special issue, we would like to express our thanks to all of the contributors for their interesting results, the anonymous referees for their thankless job and the Editorial Board of JACI, especially Prof. Kaoru Hirora, for the enthusiastic hospitality with which JACI agreed to have this special issue. Finally, we want to thank Prof. Hung T Nguyen and Prof. Michio Sugeno - for their supports to VJFUZZY'2001. Without their help, the VJFUZZY'2001 and then this special issue would not be impossible.
Fuzzy Relation with Thresholds and Applications
Authors: Cuong Bui Cong ,   Institute of Mathematics, Phuong Nguyen Hoang , Anh Phan Hoang , Yamada Koichi ,   Institute of Information Technology, Duong Hoang Quoc Viet, Nghia Do, Cau Giay, Hanoi, Vietnam,   Hanoi University of Technology, 1 Dai Co Viet, Hanoi, Vietnam,   Department of Management and Information System Science, Nagaoka University of Technology, 1603-1, Kami-tomioka, Nagaoka, Niigata, 940-2188 Japan
Published: April 2016

This paper concerns fuzzy relations with thresholds and some applications. Firstly, we consider fuzzy relations with thresholds-relation arising from many problems of real-world life, then the extension of some fuzzy inference methods using t-norms with thresholds are discussed and some calculations in SQL query forms are considered.
Reinforcement Leaning of Fuzzy Control Rules with Context-Specitic Segmentation of Actions
Authors: Yamagishi Hideki ,   Dept. of Systems Science, Graduate School of lnformatics, Kyoto University, Yoshidahon-machi, Sakyo-ku, Kyoto, 606-8501 Japan, Kawakami Hiroshi , Horiuchi Tadashi , Katai Osamu ,   Dept. of Information Engineering, Matsue National College of Technology, 14-4, Nishi-ikuma, Matsue, 690-8518 Japan
Published: April 2016

Knowledge acquisition mainly involves two approaches: deriving general or abstract rules from human expertise such as heuristics of target systems, refined properly using further information, and extracting proper rules from experimental information, i.e., information on rewards and penalties obtained from all the possible alternative rules initially prepared ‐ our approach. Reinforcement learning methods are applied to problems where meaningful I/O sets cannot be specified beforehand. There are, however few algorithms to extract heuristics for action selection by using results of reinforcement learning. We propose a way to apply symbolic processing methods such as C4.5 to results of reinforcement learning where methods of fuzzy inference are incorporated. We also derive a proper action decision tree where conditions of proper actions for agents are effectively integrated and simplified.
Difference in Areas of the Brain for Fuzzy and Crisp Calculation
Authors: Yamanoi Takahiro ,   Division of Electronics and Information Engineering, Faculty of Engineering, Hokkai Gakuen University, west 11-1-1, South 26, Central ward, Sapporo, 064-0926 Japan, Saito Masaaki , Sugeno Michio , Sanchez EIie ,   Laboratory for Language-based Intelligent Systems, Brain Science Institute, RIKEN, Hirosawa 2-1, Wako, 351-0198 Japan,   Laboratory of Medical Informatics, Faculty of Medicine, University of Marsei11e II, 27 Bd. Jean Moulin, 13385 Marsei11e Cedex5, France
Published: April 2016

We recorded event-related potentials (ERPs) by electroencephalography (EEG) during fuzzy and crisp calculation. Questions art divided into 2 types. As type A, questions were presented as sentences. Questions of type B were presented as numerical calculation. In type A, the peak latency of the EEG was around 1100ms. In type B, the peak latency was around 650ms. In type A, from multiple equivalent current dipole source localization (ECDL) around the latency, it followed that sources during fuzzy calculation lie in the right hemisphere and that sources during crisp calculation lie in the left hemisphere. In type B, no significant difference was observed.
Contribution to Creation of Complex System Macrosituations
Authors: Ocelíková Eva ,   Department of Cybernetics and Artificial Intelligence, Technical University of Kosice, Letná 9 04120 Kosice, Slovak Republic, Madarász Ladislav
Published: April 2016

This paper deals with the creation of multidimensional data classes – macrosituations – by decreasing their dimension. A large number of monitored attributes in examined situations in complex systems often complicates technical realization of classification and extends the time needed for providing a decision. It is possible to decrease the dimension of situations and, simultaneously, to not decrease decision-making quality. The main subject relates to a possible approach – the Principal Component Method. The basis of this method lies in finding a linear transformation of original p-dimensional space of attributes into a new p’-dimensional space of attributes where p’≤p. New attributes, called principal components, arise in a suitable linear combination of original attributes and are sorted in descending order based on their variance.
State Feedback Stabilization in Nonlinear Time-Delay Systems
Authors: Hori Tsuyoshi ,   Department of Mechanical Engineering and Intelligent Systems, The University of Electro-Communications, 1-5-1, Chofugaoka, Chofu, Tokyo, 182-8585 Japan, Tanaka Kazuo
Published: April 2016

In this paper, a class of nonlinear time-delay systems based on the Takagi-Sugeno (T-S) fuzzy model is defined. We investigate the delay-independent stability of this model. A model-based fuzzy stabilization design utilizing the concept of parallel distributed compensation (PDC) is employed. The main idea of the controller design is to derive each control rule to compensate each rule of a fuzzy system. Moreover, the problem of H∞ of this class of nonlinear time-delay systems is considered. The associated control synthesis problems are formulated as linear matrix inequality (LMI) problems.
Selected Papers from SCIS & ISIS 2002
Authors: Miyamoto Sadaaki ,   Institute of Engineering Mechanics and Systems University of Tsukuba, Ibaraki, 305-8573 Japan, Yasunobu Seiji
Published: April 2016

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.
Modifier Logics Based on Graded Modalities
Authors: Mattila Jorma K. ,   Lappeenranta University of Technology, Laboratory of Applied Mathematics, P.O. Box 20, FIN-53851, Lappeenranta, Finland
Published: April 2016

Modifier logics are considered as generalizations of "classical" modal logics. Thus modifier logics are so-called multimodal logics. Multimodality means here that the basic logics are modal logics with graded modalities. The interpretation of modal operators is more general, too. Leibniz’s motivating semantical ideas (see [8], p. 20-21) give justification to these generalizations. Semantics of canonical frames forms the formal semantic base for modifier logics. Several modifier systems are given. A special modifier calculus is combined from some "pure" modifier logics. Creating a topological semantics to this special modifier logic may give a basis to some kind of fuzzy topology. Modifier logics of S4-type modifiers will give a graded topological interior operator systems, and thus we have a link to fuzzy topology.
Response Control of Variable Stiffness Structure Using Electromagnetic Clutch
Authors: Irie Toshihiro ,   Nihon University, Funabashi, Chiba, 274-8501 Japan, Shingu Kiyoshi , Kitamura Keita , Takagi Yoshihiro ,   Aisin AW Corporation, Takane, Fujii, Anjo, Aichi, 444-1192 Japan
Published: April 2016

Vibration control method of a variable stiffness structure using an electromagnetic clutch is shown. The control effect is predicted by numerical simulation. On the basis of this result, an experiment is carried out using an actual apparatus to confirm the effect of the variable stiffness structure using the electromagnetic clutch.
A Powerful Neural Network Method with Digital-contract Hints for Pricing Complex Options
Authors: Lu Jun ,   Department of Industrial Engineering, Graduate School of Engineering, Osaka Prefecture University, Sakai, Osaka 599-8531, Japan, Ohta Hiroshi
Published: April 2016

Many researches have proved that common neural network methods outperform parametric methods for option pricing. However, performance of the common neural network method usually suffers from the non-stationary and noisy properties of observed financial data. In this paper, we propose some parametric digital-contract (DC) hints, which can be utilized as auxiliary information to guide a neural network’s learning process about target pricing formula, and thus can be expected to get a better pricing performance in the case of observed data with noise. The DC hints are incorporated into a neural network with serial and parallel forms. Some Monte Carlo simulation experiments are performed and demonstrated that both the two forms not only have the nonparametric method’s advantages like generalization and superior accuracy, but also have the parametric method’s robust property to financial data with noise. The results also show that these two forms have their own strengths and limitations.
Fuzzy Microaggregation for Microdata Protection
Authors: Domingo-Ferrer Josep ,   Department of Computing Engineering and Maths - ETSE, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007 Tarragona, Catalonia, Spain, Torra Vicenç ,   Institut d’Investigació en Intel-ligèencia Artificial - CSIC, Campus UAB s/n, 08193 Bellaterra, Catalonia, Spain
Published: April 2016

In this work we describe a microdata protection method based on the use of fuzzy clustering and, more specifically, using fuzzy c-means. Microaggregation is a well-known masking method for microdata protection used by National Statistical Offices. Given a set of objects described in terms of a set of variables, this method consists on building a partition of the objects and then replace the original evaluation for each variable by the aggregates of each partition. This is, the values in a given cluster are aggregated –fused– and used instead of the original ones. As the problem of finding the best partition for microdata protection is an NP problem, heuristic methods are considered in the literature. Our approach uses fuzzy c-means for building a fuzzy partition, instead of a crisp one.
An Indiscernibility-Based Clustering Method with Iterative Refinement of Equivalence Relations -Rough Clustering-
Authors: Hirano Shoji ,   Department of Medical Informatics, Shimane Medical University, School of Medicine, 89-1 Enya-cho, Izumo, Shimane 693-8501, Japan, Tsumoto Shusaku
Published: April 2016

This paper presents a new indiscernibility-based clustering method called rough clustering, that can handle relative proximity. Relative proximity is a class of proximity measures that can be used to represent subjective similarity or dissimilarity; such as human judgment about likeness of persons. Since relative proximity is not necessarily required to satisfy the triangular inequality, conventional centroid-based clustering methods may fail to produce good clusters due to inappropriate assignment of cluster representatives. Our method is based on iterative refinement of N binary classifications, where N denotes the number of objects. First, an equivalence relation, that classifies all the other objects into two classes, similar and dissimilar, is assigned by referring to their relative proximity. Next, for each pair of the objects, we count the number of binary classifications in which the pair is included in the same class. We call this number as indiscernibility degree. If the indiscernibility degree of a pair is larger than a user-defined threshold value, we modify the equivalence relations so that all of them commonly classify the pair into the same class. This process is repeated until class assignment becomes stable. Consequently, we obtain the clustering result that follows given level of granularity without using geometric measures.
A Sequential Method for Combining Random Utility Model and Fuzzy Inference Model
Authors: Lee Backjin ,   Graduate School for International Development and Cooperation, Hiroshima University, 1-5-1 Kagamiyama, Higashi-Hiroshima, 739-8529, Japan, Fujiwara Akimasa , Sugie Yoriyasu , Namgung Moon ,   Department of Civil and Environmental Engineering, Wonkwang University, 344-2 Shinyongdong, Iksanshi, Chollabukdo, 570-7491, Korea
Published: April 2016

In the analysis of choice behavior problem, uncertainty can be divided into two different types: randomness and vagueness. Random utility model and fuzzy inference model have been widely used to consider the randomness and the vagueness, respectively. Despite the necessity of simultaneously considering both uncertainties in choice behavior analysis, few literatures have tried to combine the two types of choice behavior models. Therefore, the aim of this paper is to suggest a model combining the randomness and the vagueness in the context of driver’s route choice behavior under traffic information. To estimate the combined model, a sequential method is suggested as follows: First, a latent class multinomial logit model (LCML) is developed to consider the randomness of route choice behaviors and to analyze the heterogeneity among drivers. Second, a fuzzy inference model is developed to consider the vagueness. Finally, the combined model is established by combining the estimation results of the LCML and the fuzzy inference models. The empirical results in this paper show that the combined model can contribute to enhance the explanatory power of the LCML model by effectively incorporating the randomness and the vagueness uncertainty in the choice behavior model.
Fuzzy Traffic Controller in Ramp Metering of Urban Expressway
Authors: Okushima Masashi ,   Department of Civil Engineering, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan, Takihi Yoshiharu , Akiyama Takamasa ,   Graduate School of Engineering, Gifu University
Published: April 2016

Many uncertain factors must be considered in efficiently and practically controlling traffic. Inflow control with fuzzy logic is proposed to maintain smooth traffic on urban expressways. Advanced inflow control with ramp metering is considered due to the rapid progress of ITS technology. A fuzzy inflow controller is formulated in practical ramp metering based on the above research. The advantages of fuzzy inflow controller are summarized with empirical studies.
A New Communication Method Using Natural Language as a Computer Communication Protocol
Authors: Kobayashi Ichiro ,   Department of Information Sciences, Faculty of Science, Ochanomizu University, 2-1-1 Otsuka, Bunkyo-ku, Tokyo 112-8610, Japan, Sugimoto Toru , Iwashita Shino , Iwazume Michiaki , Ozawa Jun , Sugeno Michio ,   Brain Science Institute of the Institute of Physical and Chemical Research (RIKEN),   Advanced Technology Research Laboratories, Matsushita Electric Industrial Co., Ltd.
Published: April 2016

We propose a computer communication protocol based on natural language called "language protocol", communication using the protocol, and an interface enabling connection any communication standard, called a "language application programming interface". We use simulation to confirm that the proposed methods provide a flexible communication environment for any communication object.
Some Preliminary Results on the Stableness of Extended F-rule Systems
Authors: Thuy Nguyen Thanh ,   Hanoi University of Technology, 1 DaiCoViet str., Hanoi, Vietnam, Hieu Phan Duong , Yamanoi Takahiro ,   Dept. of CS, Ecole Normale Superieure, Paris, France,   Division of Electronics and Information Engineering, Faculty of Engineering, Hokkai Gakuen University, W11-1-1, S26, Central ward, Sapporo, 064-0926 Japan
Published: April 2016

In this paper we shall investigate an extended version of F-rule systems, in which each F-rule can include an arbitrary combination of disjunctions and conjunctions of atoms in the premise. The first main result here is a way to determine values assigned to these extended facts, based on two basic operators ⊕ and x;, which are shown to be equivalent to external probabilistic reasoning by resolving linear programming problem. Based on this, a definition on mixed inference operator for extended F-rule systems is discussed. We have shown that an extended F-rule system with the defined reasoning operator is stable iff its corresponding F-rule system is stable. This proposition allows us to apply all our available research results on F-rule systems to extended F-rule systems.
The Fuzzy Description Logic ALCFH with Hedge Algebras as Concept Modifiers
Authors: Hölldobler Steffen ,   Department of Computer Science, Technische Universität Dresden, D-01062 Dresden, Germany, Störr Hans-Peter , Khang Tran Dinh ,   Faculty of Information Technology, Hanoi University of Technology, 1 Dai Co Viet, Hanoi, Vietnam
Published: April 2016

In this paper we present the fuzzy description logic ALCFH introduced, where primitive concepts are modified by means of hedges taken from hedge algebras. ALCFH is strictly more expressive than Fuzzy-ALC defined in [11]. We show that given a linearly ordered set of hedges primitive concepts can be modified to any desired degree by prefixing them with appropriate chains of hedges. Furthermore, we define a decision procedure for the unsatisfiability problem in ALCFH, and discuss knowledge base expansion when using terminologies, truth bounds, expressivity as well as complexity issues. We extend [8] by allowing modifiers on non-primitive concepts and extending the satisfiability procedure to handle concept definitions.