bluuebunny/crossref_metadata_2025 huggingface dataset:
Emergence of Learning Rule in Neural Networks Using Genetic Programming Combined with Decision Trees
Authors: Matsumoto Noboru ,   Department of Control & Systems Engineering, Toin University of Yokohama 1614 Kurogane-cho, Aoba-ku, Yokohama 225-8502, Japan, Mackin Kenneth J. , Tazaki Eiichiro
Published: April 2016

Genetic Programming (GP) combined with Decision Trees is used to evolve the structure and weights for Artificial Neural Networks (ANN). The learning rule of the decision tree is defined as a function of global information using a divide-and-conquer strategy. Learning rules with lower fitness values are replaced by new ones generated by GP techniques. The reciprocal connection between decision tree and GP emerges from the coordination of learning rules. Since there is no constraint on initial network, a more suitable network is found for a given task. Fitness values are improved using a Hybrid GP technique combining GP and Back Propagation. The proposed method is applied to medical diagnosis and results demonstrate that effective learning rules evolve.
Fuzzy Neural Models Based on Some New Fuzzy* Arithmetic Operations
Authors: Musilekl Petr ,   Intelligent Systems Research Laboratory College of Engineering, University of Saskatchewan, Saskatoon, Saskatchewan, Canada S7N 5A9, Gupta Madan M.
Published: April 2016

This paper introduces a novel approach to fuzzy arithmetic computation in fuzzy neural networks. The first part provides an overview of the standard fuzzy arithmetic operations and limitations of their use in fuzzy arithmetic based neural models. Consequently, alternative fuzzy arithmetic operations are developed and their aspects for the neural models are discussed in more detail. Originality of our approach lies in the treatment of neural inputs and weights as interactive variables which allows control of uncertainty growth in neural processing. Besides the detailed theoretical description of these operations, corresponding implementation algorithms are given as well. Combination of the alternative fuzzy arithmetic operations is briefly shown on two particular fuzzy arithmetic neurons providing fuzzy extensions of common crisp neural models. Finally, an example of a simple fuzzy neural structure for pattern classification is given.
Car Type/Name Recognition System Based on The Concept of Fixation
Authors: Arai Yoshinori ,   Research and Education Center of Software Engineering, Tokyo Institute of Polytechnics, 1583 Iiyama, Atsugi, Kanagawa 243-0297, Japan, Hirota Kaoru ,   Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8502, Japan
Published: April 2016

The concept of the "Fixation", meaning to monitor features for identifying transport vehicles is proposed. And a framework of the "Fuzzy Hierarchical Pattern Recognition using a Fixation Feedback (FHPR/FF)" " algorithm is introduced. The experiment system, for identifying car types and car names where 70 images are taken for 14 miniature cars from 5 directions, is constructed. Forced recognition of 92.9% is achieved for car types and 89.7% for car names. The proposed experiment system is the basis for real running vehicle identification confirmed by perfect identification of 5 images for each model.
Tracking Control of Variable Structure Using Fuzzy Variable Boundary Layer
Authors: Lee Heejin ,   Intelligent Control and System Lab., Dept. of Electronic Engr., Yonsei University 134, Shinchon-dong, Sudaemun-ku, Seoul, 120-749, Korea, Kim Dong-Yon , Lee Taeck-Kie , Kim Sang-Hoon , Park Mignon ,   Dept. of Control and Instrumentation Engr., Hankyong National University,   Dept. of Electronic Engr., Hankyong National University,   Dept. of Electrical Engr., Hankyong National University 67, Seokjeong-dong, Ansung, Kyonggi, 456-749, Korea
Published: April 2016

Control gain greatly affects variable structure system (VSS) performance as a system design parameter. The thin boundary layer used to eliminate chatter neighbors the sliding surface. Sliding control based on a variable boundary layer tracks better than a fixed layer. We propose variable structure control using fuzzy algorithms in control gain and the boundary layer to increase tracking efficiency, proving its feasibility in application to a simple nonlinear system.
Rule Extraction by Structural Learning with an Immediate Critic
Authors: Ishikawa Masumi ,   Department of Brain Science and Engineering Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
Published: April 2016

Studies on rule extraction using neural networks have exclusively adopted supervised learning, in which correct outputs are always given as training samples. The real world, however, does not always provide correct answers. We advocate the use of learning with an immediate critic, which is simple reinforcement learning. It uses an immediate binary reinforcement signal indicating whether or not an output is correct. This, of course, makes learning more difficult and time-consuming than supervised learning. Learning with an immediate critic alone, however, is not powerful enough in extracting rules from data because distributed representation emerges just as in back propagation learning. We propose to combine learning with an immediate critic and structural learning with forgetting (SLF) - structural learning with an immediate critic and forgetting (SLCF). A procedure of rule extraction from data by SLCF is similar to that by SLF. Applications of the proposed method to rule extraction from lenses data demonstrate its effectiveness.
A New Framework for Fuzzy Modeling Using Genetic Algorithm
Authors: Matsushita Seiichi ,   Nagoya Municipal Industrial Research Institute, Furuhashi Takeshi , Tsutsui Hiroaki ,   Dept. of Information Electronics, Nagoya University,   Yamatake Co.
Published: April 2016

This paper presents a new framework for fuzzy modeling using genetic algorithm. A model of actual object in the real world should satisfy various criteria, such as precision, generality, noise immunity, etc. It has been difficult for the fuzzy modeling to allocate proper weights on these criteria. The framework introduced in this paper consists of a model generation block and a model-testing block. The model generation block generates candidates of fuzzy model under criteria with higher importance, and the model-testing block tests the candidates under notso-important criteria. This division of criteria can put emphasis on the criteria in the generation block and less on those in the testing block. Simulations are done to show the effectiveness of the proposed framework.
Linguistic Rule Extraction from Numerical Data for High-dimensional Classification Problems
Authors: Ishibuchi Hisao ,   Department of Industrial Engineering, Osaka Prefecture University Sakai, Osaka 599-8531, Japan, Murata Tadahiko , Nakashima Tomoharu ,   Department of Industrial and Information Systems Engineering, Ashikaga Institute of Technology Ashikaga, Tochigi 326-8558, Japan
Published: April 2016

We discuss the linguistic rule extraction from numerical data for high-dimensional classification problems. Difficulties in the handling of high-dimensional problems stem from the curse of dimensionality: the number of combinations of antecedent linguistic values exponentially increases as the number of attributes increases. Our goal is to extract a small number of simple linguistic rules with high classification ability. In this paper, the rule extraction is to find a set of linguistic rules using three criteria: its classification ability, its compactness, and the simplicity of each rule. Our approach consists of two phases: candidate rule generation and rule selection. We first propose a pre-screening method for generating a tractable number of promising candidate rules for high-dimensional classification problems where it is impossible to examine all combinations of antecedent linguistic values. Next we show how genetic algorithms can be applied to the rule selection. Then we combine a heuristic rule elimination procedure with genetic algorithms for improving their search ability. Finally, the performance of our approach is examined by computer simulations on commonly used data sets.
Assessing the Relevance of Processing Building Blocks in Evolutionary Computation: Experiments with Linear Systems of Equations
Authors: Fogel David B. ,   Natural Selection, Inc. 3333 N. Torrey Pines Ct., Suite 200, La Jolla, CA 92037, Angeline Peter J. ,   Natural Selection, Inc. 509 Colgate St. Vestal, NY 13850
Published: April 2016

Experiments are conducted to assess the utility of processing building blocks within a framework of evolutionary computation. Systems of linear equations are used for testing the efficiency of different recombination operators, including one- and two-point and uniform crossover. The consistent results indicate that uniform crossover, which disrupts building blocks maximally, generates statistically significantly better solutions than one- or two-point crossover. Moreover, for the cases of small population sizes, crossing over existing solutions with completely random solutions (i.e., macromutation) can perform as well or better than the traditional oneand two-point operators. The results do not support the building block hypothesis.
Fuzzy Cognitive Map Approach to Process Control Systems Chrysostomos
Authors: Stylios D. ,   Laboratory for Automation and Robotics Department of Electrical and Computer Engineering University of Patras Rion 26500, Greece, Groumpos Peter P , Georgopoulos Voula C. ,   Technological Educational Institute of Patras Koukouli 26334, Patras, Greece
Published: April 2016

We propose an approach to describe and control complex systems based on fuzzy cognitive map (FCM). A mathematical model of FCMs and a calculation method are described as well as a methodology for constructing and developing them that exploits experts who use fuzzy rules to explain cause and effect among concepts. We apply FCMs in a process control problem and demonstrate their effectiveness. We propose a two-level structure for supervisory control of the process, where the supervisor is modeled as an FCM used for failure detection and decision analysis. There is increasing demand for more autonomous, intelligent systems, and the application of FCMs in control and systems may contribute in developing such systems.
Evolving Cooperative Groups Using Shared Memory
Authors: Puppala Narendra ,   Visonael Corporation, Sen Sandip ,   Department of Mathematical and Computer Science, University of Tulsa 600 South College Avenue, Tulsa, OK 74104-3189, U. S. A
Published: April 2016

We present a coevolutionary approach to generating behavioral strategies for cooperating agent groups. We coevolve agent behavior with genetic algorithms (GAs), where one GA population is evolved per individual in the cooperative group. Groups are evaluated by pairing strategies from each population and the best strategy pairs are stored together in shared memory. To evaluate a strategy from one population, it is paired sequentially with strategies from the other population stored in shared memory. The maximum evaluation from all such pairings is used to evaluate the strategy. A pair is stored in shared memory if we encounter at least one pair previously stored there with a lower fitness value. We evaluate our approach using an asymmetric room painting domain with two agents - a whitewasher and a painter. Shared memory proved superior to random pairing in consistently generating optimal behavior patterns.
Adaptive Reinforcement Learning Integrating Exploitation-and Exploration-oriented Learning
Authors: Kurihara Satoshi ,   NTT Network Innovation Laboratories 3-9-11 Midori-Cho, Musashino-Shi, Tokyo, 180-8585 Japan Tel: +81 422 59 4139, Fax: +81 422 59 2225, Onai Rikio , Sugawara Toshiharu ,   NTT Software Corporation 209 Yamashita-cho Naka-ku Yokohama-shi, Kanagawa 231-8551 Japan
Published: April 2016

We propose and evaluate an adaptive reinforcement learning system that integrates both exploitation- and exploration-oriented learning (ArLee). Compared to conventional reinforcement learning, ArLee is more robust in a dynamically changing environment and conducts exploration-oriented learning efficiently even in a large-scale environment. It is thus well suited for autonomous systems, for example, software agents and mobile robots, that operate in dynamic, large-scale environments, such as the real world and the Internet. Simulation demonstrates the learning system’s basic effectiveness.
Probabilistic Learning-Network-Based Robust Control Scheme for Nonlinear Systems
Authors: Hu Jinglu ,   Department of Electrical and Electronic Systems Engineering Graduate School of Information Science and Electrical Engineering Kyushu University, Hirasawa Kotaro , Murata Junichi , Jin Chunzhi , Matsuoka Takuya ,   Engine Engineering Division II, Toyota Motor Corporation
Published: April 2016

We present a control design scheme for nonlinear systems based on a probability learning network (ProNet). ProNet is a learning network equipped with the capability to deal with stochastic signals. A plant and its controllers are described by using a set of related equations and form a unified learning network-ProNet where disturbances are considered as external inputs. In this way, controller design is transferred to ProNet learning. By including an effort to reduce variances of ProNet output in the criterion function for training, the trained ProNet has different sensitivities to signals of different frequencies. A ProNet control system is designed by taking this advantage to increase its robustness against disturbances. Computer simulations confirm the effectiveness of the ProNet control scheme.
Fuzzy Control of a Direct Current Motor System and Stability Analysis
Authors: Kim Euntai ,   Department of Control and Instrumentation Engineering, Hankyong National University 67 Sukjung dong, Ansung, Kyungki, 456-749, Korea Tel. +82-31-670-5295, Fax: +82-334-670-5015, Lee Heejin , Kim Dongyon ,   Department of Electronic Engineering, Hankyong National University
Published: April 2016

One of the most common ways of driving electromechanical systems is through the use of a DC motor. In this paper, fuzzy control methodology for a DC motor system using a singleton fuzzy logic controller (FLC) is proposed. As opposed to conventional works, fuzzy control methodology proposed here is guaranteed to be asymptotically stable on the whole. Finally, the validity of the suggested methodology is highlighted via an illustrative example.
Neuro-Classification of Currency Fatigue Levels Based on Acoustic Cepstrum Patterns
Authors: Teranishi Masaru ,   Department of Electrical Engineering, Nara National College of Technology 22 Yata-cho, Yamatokoriyama, Nara 639-1080, Japan, Omatu sigeru , Kosaka Toshihisa ,   Department of Computer and Systems Sciences, College of Engineering, Osaka Prefecture University Gakuencho, Sakai, Osaka 599-8531, Japan,   Glory Ltd. 1-3-1 Shimoteno, Himeji 670-8567, Japan
Published: April 2016

This paper proposes a new method to classify currencies into different fatigue levels. Acoustic cepstrum patterns obtained from an acoustic signal generated by a currency passing through a banking machine are used for classification. The acoustic cepstrum patterns are fed to a competitive neural network with the Learning Vector Quantization (LVQ) algorithm, and classified the currency into three fatigue levels. The experimental results show that the proposed method is useful for classification of fatigue levels of currencies, and the LVQ algorithm performs a good classification.
Using Rough Sets for Practical Feature Selection in a Rough Sets/Neural Network Framework for Knowledge Discovery
Authors: Jagielska Ilona ,   School of Information Management & Systems, Monash University PO BOX 197, 26 Sir John Monash Drive, Caulfield East, Victoria 3145, Australia
Published: April 2016

An important task in knowledge discovery is feature selection. This paper describes a practical approach to feature subset selection proposed as part of a hybrid rough sets/neural network framework for knowledge discovery for decision support. In this framework neural networks and rough sets are combined and used cooperatively during the system life cycle. The reason for combining rough sets with neural networks in the proposed framework is twofold. Firstly, rough sets based systems provide domain knowledge expressed in the form of If-then rules as well as tools for data analysis. Secondly, rough sets are used in this framework in the task of feature selection for neural network models. This paper examines the feature selection aspect of the framework. An empirical study that tested the approach on artificial datasets and real-world datasets was carried out. Experimental results indicate that the proposed approach can improve the performance of neural network models. The framework was also applied in the development of a real-world decision support system. The experience with this application has shown that the approach can support the users in the task of feature selection.
Procedural Knowledge Processing Based on Area Representation Using a Neural Network
Authors: Fujinaga Seiya ,   Department of Electrical Engineering, Faculty of Science and Technology, Keio University 3-141 Hiyoshi, Kohoku-ku, Yokohama 223-8522 Japan, Hagiwara Masafumi
Published: April 2016

In this paper, a neural network that treats procedural knowledge based on area representation is proposed. The main theme of this paper is to propose a novel neural network that processes procedural knowledge. The network employs formerly proposed ideas such as "area representation" and "improved Hebbian learning." Area representation expresses information by a group of neurons. Since it is considered as a combination of localized and distributed representation, it has many advantages such as robustness, high efficiency for information representation, and potential ability to treat similarity of data. The proposed network based on area representation is constructed to store and recall procedural knowledge. We performed various kinds of computer simulations to examine the validity and effectiveness of the proposed network.
Application of Choquet Integral to Efficient Aggregation of Neural Networks
Authors: Cho Sung-Bae ,   Department of Computer Science, Yonsei University 134 Shinchon-dong, Sudaemoon-ku, Seoul
Published: April 2016

This paper presents a novel application of fuzzy integral to combine multiple neural networks subjectively. The concept of combining multiple neural networks or classifiers has been largely exploited in the fields of artificial neural networks and pattern recognition. The proposed method based on Choquet integral nonlinearly combines the outputs of the networks with subjective evaluation of the reliability of the individual neural networks. Experimental results with the recognition problem of totally unconstrained handwritten numerals show that the performance of the proposed method outperforms that of the conventional methods.
Differentiation of the Choquet Integral and Its Application to Long-term Debt Ratings
Authors: Kaino Toshihiro ,   Department of Computational Intelligence and Systems Science, Hirota Kaoru
Published: April 2016

Differentiation of the Choquet integral of a nonnegative measurable function with respect to a fuzzy measure on fuzzy measure space is proposed and it is applied to the capital investment decision making problem by Kaino and Hirota. In this paper, differentiation of the Choquet integral of a nonnegative measurable function is extended to differentiation of the Sipos Choquet integral of a measurable function and its properties will be discussed. First, the real interval limited Schmeidler Choquet integral and Sipos Choquet integral are defined for preparation, then the upper differential coefficient, the lower differential coefficient, the differential coefficient, and the derived function of the Choquet integral along the range of an integrated function are defined by the limitation process of the interval limited Choquet integral. Two examples are given, where the measurable functions are either a simple function or a triangular function. Basic properties of differentiation about swn and multiple with constant, addition, subtraction, multiplication, and division are shown. Then, the Choquet integral is applied to long-term debt ratings model, where the input is qualitative and quantitative data of corporations, and the output is Moody’s long-term debt ratings. The fuzzy measure, which is given as the importance of each qualitative and quantitative data, is derived from a neural net method. Moreover, differentiation of the Choquet integral is applied to the long-term debt ratings, where this differentiation indicates how much evaluation of each specification influence to the rating of the corporation.
A Genetic Algorithm for Machines Sequencing Considering Operation Flexibility and AGV Guidepath
Authors: Moon Chiung ,   Department of Information and Industrial Engineering Hanyang University, Ansan 425-791, Korea, Gen Mitsuo ,   Department of Industrial & Information Systems Engineering Ashikaga Institute of Technology, Ashikaga 326-8558, Japan
Published: April 2016

In this paper, we propose a model for machines sequencing considering operation flexibility and automated guided vehicle (AGV) guidepath for computer-aided process planning system. The machine sequences for all parts should be selected based on the following multiple criteria: (1) minimizing the sum of machine processing time and the material handling time of all the parts considering production volume, machining time, machine capacity, material handling time, and capacity of transfer device and (2) balancing workload between machines. A genetic algorithm is developed to solve the problem and numerical examples are presented to illustrate the efficiency of proposed approach.
Representation and Propagation of Information Granules in Rule-based Computing
Authors: Pedrycz Witold ,   Department of Electrical & Computer Engineering, University of Alberta, Edmonton T6G 2G7 Canada Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland, Vulcovich George ,   Canadian Space Agency, Spacecraft Engineering 6767 Route de I’Aeroport Saint-Hubert, Quebec J3Y 8Y9 Canada
Published: April 2016

The study is devoted to the paradigm of rule based computing involving granular information. By information granules we mean a general category of data embracing not only numeric entities (inputs) but any granules (such as intervals or fuzzy sets, in general) being regarded as inputs in the rule-based system. We investigate several categories of models of granularity propagation starting from those based on the use of the mechanisms of possibility and necessity theory, especially possibility and possibility-necessity mechanisms. We also consider the models relying on the use of auxiliary regression models. These models are constructed on the basis of some experimental granular data. A thorough comparative analysis of the introduced models is carried out as well.
Pattern and Feature Selection by Genetic Algorithms in Nearest Neighbor Classification
Authors: Ishibuchi Hisao ,   Department of Industrial Engineering, Osaka Prefecture University Gakuen-cho 1-1, Sakai, Osaka 599-8531, Japan, Nakashima Tomoharu
Published: April 2016

This paper proposes a genetic-algorithm-based approach for finding a compact reference set in nearest neighbor classification. The reference set is designed by selecting a small number of reference patterns from a large number of training patterns using a genetic algorithm. The genetic algorithm also removes unnecessary features. The reference set in our nearest neighbor classification consists of selected patterns with selected features. A binary string is used for representing the inclusion (or exclusion) of each pattern and feature in the reference set. Our goal is to minimize the number of selected patterns, to minimize the number of selected features, and to maximize the classification performance of the reference set. Computer simulations on commonly used data sets examine the effectiveness of our approach.
Multiple Lagrange Multiplier Method for Constrained Evolutionary Optimization
Authors: Myung Hyun ,   Virtual Reality R&D Center, ETRI 161 Kajong-dong Yusong-gu, Taejon 305-350, Korea, Kim Jong-Hwan ,   Department of EE, KAIST 373-1 Kusong-dong, Yusong-gu, Taejon 305-701, Korea
Published: April 2016

One of the well-known problems in evolutionary search for solving optimization problem is the premature convergence. The general constrained optimization techniques such as hybrid evolutionary programming, two-phase evolutionary programming, and Evolian algorithms are not safe from the same problem in the first phase. To overcome this problem, we apply the sharing function to the Evolian algorithm and propose to use the multiple Lagrange multiplier method for the subsequent phases of Evolian. The method develops Lagrange multipliers in each subpopulation region independently and seeks for multiple global optima, if any, in parallel. The simulation results demonstrate the usefulness of the proposed multiple Lagrange multiplier method.
Neural Network Based Power Flow Predictor
Authors: Madan S. ,   Qualcomm Incorporated 5775 Morehouse Dr. San Diego, CA 92121, Bollinger K. E. ,   Department of Electrical & Computer Engineering, University of Alberta Edmonton, A13, T6G 2G7
Published: April 2016

This paper describes the development of a synergistic intelligent system for power system management. One component of this system is a neural network (NN) based software utility, which predicts power flows over transmission lines and transformers. This paper focuses on the NN predictor
ARTMAP Neural Networks for Multispectral Image Classification
Authors: Kopco Norbert ,   Department of Cognitive and Neural Systems, Boston University, 677 Beacon St., Boston, MA 02215, the U.S.A. And CIG, KKUI, FEI TU Kosice, Letna 9, 04001 Kosice, Slovak Rep., Sincak Peter , Kaleta Stanislav ,   Computational Intelligence Group, Department of Cybernetics and AI Faculty of EE and Informatics, Technical University, Letna 9, 04001 Kosice, Slovak Republic
Published: April 2016

This paper presents an analysis of performance of several types of the ARTMAP neural network. The performance of the networks is analyzed in the task of classification of satellite images obtained by remote sensing. The analysis is concentrated on the dependence of classification accuracy on the difference in cluster type preferably identified by each of the classifiers. Three types of ARTMAP classifier are compared: fuzzy ARTMAP, Gaussian ARTMAP, and Extended Gaussian ARTMAP The main difference among these classifiers is in the way they determine/represent individual clusters in feature space. Best results are obtained for Extended Gaussian ARTMAP, a modification of the Gaussian ARTMAP neural network that preferably identifies Gaussian-distributed clusters.
Approach to Scheduling Problem Solution in Production Systems Using the Multiagent System
Authors: Frankoviè  ,   Institute of Control Theory and Robotics, B.  , Labátová S.  , Budinská  , I.
Published: April 2016

This paper considers job-shop-scheduling problem in multimachine multipart manufacturing systems. The purpose of the article is to contribute to the decision on scheduling rules for job-shop problems. The paper also describes the possibility of utilization of MAS formalism to represent different parts of the production systems and their mutual relations. The interrelations in the multiagent world are examined.
Design of a Single-input Adaptive Fuzzy Logic Controller using a Switching Hyperplane
Authors: Choi Byung-Jae ,   School of Computer and Communication Eng., Taegu University,, Kwak Seong-Woo , Kim Byung Kook ,   Department of Electrical Engineering, KAIST
Published: April 2016

Most fuzzy logic controllers (FLCs) for minimum phase plants use an UNLP (Upper Negative and Lower Positive) or UPLN type control rule table. This property allows design of a single-input FLC using a sole input fuzzy variable, which is called the SFLC (single-input FLC). It greatly simplifies the design procedure of the conventional FLC and has many advantages. However, it is still difficult to adapt to varying operating conditions. We here design a single-input adaptive fuzzy logic controller (SAFLC) using a switching hyperplane introduced in sliding mode control. In the proposed SAFLC, some parameters of membership functions characterizing linguistic terms of fuzzy rules are adjusted by an adaptive law that directly incorporates linguistic fuzzy control rules into the controller. We also prove that 1) the closed-loop system is globally stable in the sense that all signals involved are bounded and 2) its tracking error converges to zero asymptotically. We perform computer simulation using an inverted pendulum system.
New 2-DOF PID Controller Tuning by Adaptive Neural Fuzzy Inference System for Gas Turbine Control System
Authors: Kim Dong Hwa ,   Department of I&C, Taejon National University of Technology 16-1 San Duck myong-dong Yusung-gu Taejon city Seoul Korea, 305-320. Tel:+82-42-821-1170, Fax:+82-42-821-1164, Jung Chang Kee ,   Korea Power Research Institute Tel: +82-42-865-5251
Published: April 2016

The purpose of introducing a combined cycle with gas turbines in power plants is to reduce loss of energy. Their main role lies in the utilization of waste heat that may be found in exhaust gases from the gas turbine or at some other points of the process to produce additional electricity. The efficiency of the plant exceeds 50%, while the traditional steam turbine plants is approximately 35% ∼ 40% or so. To date, the PID controller has been used to operate under such systems, but since PID controller gain manually has to be tuned by trial and error procedures, getting optimal PID gains is very difficult manually without control design experience. We studied acquiring transfer function from operating data on the Gun-san gas turbine in Korea and a new 2-DOF PID controller tuning by ANFIS is designed for the optimum control of the Guns-san gas turbine’s variables. Since the shape of a membership function in the ANFIS vary on the characteristics of plant, ANFIS-based control is effective for plants whose variables vary. Its results are compared to the conventional 2-DOF PID controller and represents satisfactory response. We expect this method will be used for another process because it is studied using actual operating data.
Self-Learning Fuzzy Logic Controller using Q-Learning
Authors: Kim Min-Soeng ,   Department of Electrical Engineering and Computer Science Korea Advanced Institute of Science and Technology 373-1 Kusong-dong Yusong-gu Taejon 305-701 Korea Telephone : +82-42-869-3432,, Hong Sun-Gi , Lee Ju-Jang
Published: April 2016

Fuzzy logic controllers consist of if-then fuzzy rules generally adopted from a priori expert knowledge. However, it is not always easy or cheap to obtain expert knowledge. Q-learning can be used to acquire knowledge from experiences even without the model of the environment. The conventional Q-learning algorithm cannot deal with continuous states and continuous actions. However, the fuzzy logic controller can inherently receive continuous input values and generate continuous output values. Thus, in this paper, the Q-learning algorithm is incorporated into the fuzzy logic controller to compensate for each method’s disadvantages. Modified fuzzy rules are proposed in order to incorporate the Q-learning algorithm into the fuzzy logic controller. This combination results in the fuzzy logic controller that can learn through experience. Since Q-values in Q-learning are functional values of the state and the action, we cannot directly apply the conventional Q-learning algorithm to the proposed fuzzy logic controller. Interpolation is used in each modified fuzzy rule so that the Q-value is updatable.
Type-2 Fuzzy Hypergraphs Using Type-2 Fuzzy Sets
Authors: Park Seihwan ,   Division of Computer Science, Department of Electrical Engineering & Computer Science Advanced Information Technology Research Center (AITrc) Korea Advanced Institute of Science and Technology (KAIST) 373-1, Kusong-dong, Yusong-gu, Taejon 305-701, Korea, Lee-Kwang Hyung
Published: April 2016

Hypergraphs have been applied to analyze system architecture and to represent system partitions. The concept of ordinary hypergraphs was extended to fuzzy and interval-valued fuzzy hypergraphs. In this paper, we extend fuzzy hypergraphs to type-2 fuzzy hypergraphs. Type-2 fuzzy hypergraphs are defined using type-2 fuzzy sets and satisfy the properties of hypergraphs as well as additional properties. We also show that interval-valued fuzzy hypergraphs are a special case of proposed type-2 fuzzy hypergraphs. The properties of type-2 fuzzy hypergraphs are explained using simple examples.
Rule Reduction and Robust Control of Generalized Takagi-Sugeno Fuzzy Systems
Authors: Taniguchi Tadanari ,   Department of Mechanical Engineering and Intelligent Systems Department of Electrical and Computer Engineering, Tanaka Kazuo ,   Department of Mechanical Engineering and Intelligent Systems The University of Electro-Communications 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585 Japan’ Tel. & Fax. +81-424-43-5425
Published: April 2016

This paper presents model reduction and robust control using a generalized form of Takagi-Sugeno fuzzy systems. We first define a generalized form of TakagiSugeno fuzzy systems. The generalized form has a decomposed structure for each element of Ai and Bi matrices in consequent parts. The key feature of this structure is that it is suitable for reducing the number of rules. Conditions to reduce the number of rules are represented in terms of linear matrix inequality (LMIs). The main idea is to find a structure of if-then rules of the reduced model that agrees well with dynamics of the original model. Furthermore, we estimate the lower bound of the norm of model uncertainty of the Takagi-Sugeno fuzzy system that can cover the reduction error. Finally, we present an example of model reduction and robust control for a nonlinear system. In this example, we achieve a robust controller design to compensate for the uncertainly of the Takagi-Sugeno fuzzy system.
Fuzzy Flip-Flops and their Applications to Fuzzy Memory Element and Circuit Design using FPGA
Authors: Yoshida Shin-ichi ,   Deptartment of Computational Intelligence and Systems Science, Takama Yasufumi , Hirota Kaoru
Published: April 2016

D, T, and SR fuzzy flip-flops are proposed and their characteristics are shown in four - max-min, algebraic, bounded, drastic - logical operation systems. The circuits of the proposed flip-flops are designed and simulated on a Synopsys circuit simulator. The result of synthesis show the areas of D, T, SR fuzzy flip-flops are nearly 0, 2/3 1/2 of that of conventional JK fuzzy flip-flops and the delay times of D, T, SR fuzzy flip-flops are nearly 0, 2/3, 2/3 of that of the JK type. Moreover, max-min fuzzy memory element based on functions required for memory is proposed independently of fuzzy flip-flops. As it uses 1/2 circuit area of the SR fuzzy flip-flops, it is more suitable for fuzzy memory element than other fuzzy flipflops.
A Methodology for Developing Adaptive Fuzzy Cognitive Maps for Decision Support
Authors: Khan M. Shamim ,   School of Information Technology, Murdoch University Perth, WA 6150, Australia, Chong Alex , Gedeon Tom
Published: April 2016

Differential Hebbian Learning (DHL) was proposed by Kosko as an unsupervised learning scheme for Fuzzy Cognitive Maps (FCMs). DHL can be used with a sequence of state vectors to adapt the causal link strengths of an FCM. However, it does not guarantee learning of the sequence by the FCM and no concrete procedures for the use of DHL has been developed. In this paper a formal methodology is proposed for using DHL in the development of FCMs in a decision support context. The four steps in the methodology are: (1) Creation of a crisp cognitive map; (2) Identification of event sequences for use in DHL; (3) Event sequence encoding using DHL; (4) Revision of the trained FCM. Feasibility of the proposed methodology is demonstrated with an example involving a dynamic system with feedback based on a real-life scenario.
Improving the Approximation Smoothness of Radial Basis Neural Networks
Authors: Little Anthony ,   School of Communications & Informatics, Victoria University of Technology, P.O. Box 14428, Melbourne City MC, VIC 8001 Australia, Reznik Leonid
Published: April 2016

Implementation of industrial multimedia and control systems, which can be described as non-linear two-dimensional surfaces, may be expensive in terms of both hardware and software requirements. Radial Basis neural networks are excellent platforms for implementing approximations of such surfaces. This paper proposes an alternative basis function in an effort to improve the ability of the network to provide a smooth surface. The resulting basis function presents a balanced approach to the integration between hidden layer neurons, while maintaining a computational structure suitable for low cost software implementation. The paper investigates the proposed structure in regard to different criteria
Topic-based Intelligent Support System for Information Retrieval
Authors: Takama Yasufumi ,   Interdisciplinary Graduate School of Science and Engineering Tokyo Institute of Technology, Yokohama, Hirota Kaoru
Published: April 2016

We propose a new concept of intelligent support systems for topic-based information retrieval. As information retrieval (IR) on the World Wide Web (WWW) becomes widespread, new types of tools and systems that do not only find specific pages the user wants, but also and helping the user learn about a particular field of interest are increasingly needed. Two systems based on this consideration are introduced in this paper. One is the Fish View system for supporting document-ordering. It focuses on the user’s document-ordering (making diagrams) while reading, and the user’s viewpoint is represented by a combination of a small number of concepts taken from the existing concept structure dictionary. The extracted viewpoint can be used for measuring the similarity among documents, using fisheye matching, the extended Vector Space Model. The other is the query network for visualization of the topic distribution through WWW IR, and its concept employing the Immune Network model is introduced with preliminary experiments.
Optimizing and Learning Algorithm for Feed-forward Neural Networks
Authors: Bachiller Pilar ,   Department of Computer Science. University of Extremadura. Escuela Politécnica., 10.071. Cáceres. Spain, González Julia
Published: April 2016

Feed-forward neural networks have emerged as a good solution for many problems, such as classification, recognition and identification, and signal processing. However, the importance of selecting an adequate hidden structure for this neural model should not be underestimated. When the hidden structure of the network is too large and complex for the model being developed, the network may tend to memorize input and output sets rather than learning relationships between them. Such a network may train well but test poorly when inputs outside the training set are presented. In addition, training time will significantly increase when the network is unnecessarily large and complex. Most of the proposed solutions to this problem consist of training a larger than necessary network, pruning unnecessary links and nodes and retraining the reduced network. We propose a new method to optimize the size of a feed-forward neural network using orthogonal transformations. This approach prunes unnecessary nodes during the training process, avoiding the retraining phase of the reduced network, which is necessary in most pruning techniques.
A New Fuzzy Controller for Stabilizing Inverted Pendulums Based on Single Input Rule Modules Dynamically Connected Fuzzy Inference Model
Authors: Yi Jianqiang ,   Institute of Automation, Chinese Academy of Sciences, P.O.Box 2728, Beijing 1ccc80, China, Yubazaki Naoyoshi , Hirota Kaoru ,   Technology Research Center, Mycom, Inc. 12, S. Shimobano, Saga Hirosawa, Ukyo, Kyoto 616-8303, Japan,   Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, 4259 Nagatsuta, Midori, Yokohama 226-8502, Japan
Published: April 2016

A fuzzy controller is presented based on the Single Input Rule Modules (SIRMs) dynamically connected fuzzy inference model for stabilization control of inverted pendulums. The angle and angular velocity of the pendulum and the position and velocity of the cart are selected as input items and the driving force as the output item. By using SIRMs and dynamic importance degrees, the fuzzy controller realizes angular control of the pendulum and position control of the cart in parallel with totally only 24 fuzzy rules. Switching between angular control of the pendulum and position control of the cart is smoothly performed by automatically adjusting dynamic importance degrees according to control situations. For any inverted pendulums, of which the pendulum length is among [0.5m, 2.2m], simulation results show that the proposed fuzzy controller has a high generalization ability to stabilize the pendulum systems completely in about 6.0 seconds when the initial angle of the pendulum is among [-30.0°, +30.0°], or the initial position of the cart is among [-2.1m, +2.1m].
Advanced Robot Control Algorithms Based on Fuzzy, Neural and Genetic Methods
Authors: Pletl Szilveszter ,   Institute of Electro-Mechanical Systems Neuro-Fuzzy-Genetic Intelligent Control Research Center, 24000 Subotica, Marka Oreskovica 16, Yugoslavia, Lantos Bela ,   Department of Control Engineering and Information Technology Budapest University of Technology and Economics H-1117 Budapest, Pazmany Peter setany 1/D, Hungary
Published: April 2016

Soft computing (fuzzy systems, neural networks and genetic algorithms) can solve difficult problems, especially non-linear control problems such as robot control. In the paper two algorithms have been presented for the nonlinear control of robots. The first algorithm applies a new neural network based controller structure and a learning method with stability guarantee. The controller consists of the nonlinear prefilter, the feedforward neural network and feadback PD controllers. The fast learning algorithm of the neural network is based on Moore-Penrose pseudoinverse technique. The second algorithm is based on a decentralized hierarchical neuro-fuzzy controller structure. New approach to evolutionary algorithms called LEGA optimizes the controller during the teaching period. LEGA combines the standard GA technique with numerical optimum seeking for a limited number of elite individuels in each generation. It can lead to global optimum in few generations. The soft computing based nonlinear control algorithms have been applied for the control of a rigid link flexible joint (RLFJ) 4 DOF SCARA robot in order to prove the effectiveness of the proposed methods.
Capital Budgeting with Multiple Criteria and Multiple Decision Makers: A Fuzzy Approach
Authors: Shi Yong ,   College of Information Science and Technology University of Nebraska at Omaha Omaha, NE 68182, Kwak Wikil , Lee Heeseok , Lee Cheng-few ,   Department of Professional Accounting College of Business Administration University of Nebraska at Omaha Omaha, NE 68182,   Department of Management Information Systems Korea Advanced Institute of Science and Technology, Seoul, South Korea 207-43,   Department of Finance School of Business Rutgers University New Brunswick, NJ 08903
Published: April 2016

A capital budgeting model with multiple criteria and multiple decision makers (MCMDM) is more likely to provide realistic solutions than linear or goal programming models. This paper adopts a fuzzy approach to solve MCMDM capital budgeting problems. This approach is based on two fundamental human cognitive processes: (i) all decision makers who are involved in the capital budgeting problem have goal setting and compromise behavior for seeking multiple criteria, and (ii) each decision maker has a preference for the budget availability level. A solution procedure is proposed to systematically identify a fuzzy optimal selection of possible projects that can not only reach the best compromise value for the multiple criteria, but also use the best budget availability level according to the multiple decision makers’ preferences. The optimal selection can help the firm make a realistic decision regarding its strategic investment. A comparison study of the fuzzy approach with other approaches shows the advantages of using the fuzzy approach.
Application of Adaptive Fuzzy Sliding Mode Control for Bridges
Authors: Yeh Ken ,   Department of Civil Engineering, De Lin Institute of Technology, Tu-Cheng, Taipei County, Taiwan, ROC., Chiang Wei-Ling ,   Department of Civil Engineering, National Central University, Chungli, Taiwan, ROC.
Published: April 2016

This paper applies adaptive fuzzy sliding mode control of structural control for bridges. Combining fuzzy control and sliding mode control can reduce the complexity of fuzzy rule base. It also ensures stability and robustness. The Lyapunov theory is used to develop adaptive law. adaptive sliding mode control is applied to 3 types of bridge, with LRB (Lead Rubber Bearing), sliding bearing isolators and no isolation device. The condition of stiffness uncertainty and time delay is used to show the robustness of this algorithm. The effectiveness of this algorithm is demonstrated by simulation results for a long period and a wide-pass white noise artificial earthquake. All these simulations are shown that adaptive sliding mode control can achieve satisfactory results in the application of structural control for bridges.
An Adaptive Fuzzy Clustering Technique for Traffic Prediction of Packet-switched Networks
Authors: Kuo Yau-Hwang ,   Department of Computer Science and Information Engineering, National Cheng Kung University, No.1, Ta-Hsueh Road, Taiwan city, Taiwan., Horng Mong-Fong , Chiang Jung-Hsien
Published: April 2016

Traffic prediction is significant to QoS design because it assists efficient management of network resources to improve the reliability and performance of the next generation Internet. The unavoidable traffic variation caused by diverse Internet services complicates traffic prediction, particularly in a multi-hop network. To simplify the complicated statistical analysis used in traditional approaches, an adaptive traffic prediction approach featuring robustness, high accuracy and high adaptability is proposed in this paper. The proposed approach bases on a novel fuzzy clustering algorithm to generalize and unveil the hidden structure of traffic patterns. The unveiled structure represents the characteristics of the target traffic. Therefore, it can be referenced to predict traffic in a limited time period by fuzzy matching. To track the variation of target traffic, the proposed approach adopts an incremental and dynamic on-line clustering procedure so that the prediction can maintain high accuracy under traffic variation. To verify the performance of the proposed approach and investigate its properties, the periodical, Poisson and real video traffic patterns have been used to experiment. The experimental results showed an excellent performance of the developed adaptive predictor. The prediction errors, in average, are near 2.2%, 13.6% and 7.62% for periodical, Poisson and real video traffics, respectively.