bluuebunny/crossref_metadata_2025 huggingface dataset:
Intelligent Control for Container Terminal AGV
Authors: Ito Masanori ,   Dept. of Electronic & Mechanical Engineering, Tokyo University of Mercantile Marine, 2-1-6 Etchujima, Koto-ku, Tokyo 135, Japan, Zhang Feifei
Published: April 2016

The world's container cargo trading is increasing daily, and the role of the container terminal is becoming more important as the center of cargo transportation. In Japan, new container terminals being constructed face very severe competition with larger, cheaper terminals so they must handle cargo more efficiently and cheaply. To cope, handling systems such as container cranes, yard cranes, and conveyers are being automated to enable unattended operation unloading and loading schedule planning, etc., are being computerized. In these system, crane automation and control computerization are already generalized, but automatic control of container conveyers is not completed yet. The container conveyer -15m long, 4m wide, and 1.5m high - picks up containers from container ships with a container crane and hauls them to the shift yard for release to the yard crane. Both crane are operated automatically, so the conveyer must stop at the desired position within a permissible error of ±7.5cm, and run on a predetermined course and speed. Collision avoidance is required because many vehicles oparate on the same course. The automated guided vehicle (AGV) system, which is diesel-driven, 4WS and 4WD, was thought to be effective, but container weights very widely, as do road conditions which depend on weather, so conventional control is not sufficient to maintain the required accuracy. We applied learning control to maintain the desired course and for stopping at the desired position. Speed was controlled, conventionally. The system's applicability was confirmed with computer simulation and vehicle performance testing. This system will be used at the Kawasaki container terminal in 1999 and we are currently working on improving performance.
Some Applications of Soft Computing Methods in System Modeling and Control
Authors: Lantos Bela ,   Department of Control Engineering and Information Technology Technical University of Budapest, H-1111 Budapest, Muegyetem rkp. 9, Hungary
Published: April 2016

The paper deals with the application of fuzzy systems, artificial neural networks (neural systems), and genetic algorithms to solve modeling and control problems in system engineering. Part 1 the paper covers the design of classical PID and fuzzy PID controllers for nonlinear systems with an (approximately) known dynamic model. Optimal controllers are designed based on genetic algorithms. Part 2 considers neural control of a SCARA robot. Part 3 deals with the fuzzy control of a special class of MIMO nonlinear systems and generalizes the method of Wang for such systems.
Common-sense Knowledge Representation and Reasoning, and its Application to Face Detection
Authors: Kouzani Abbas Z. ,   School of Engineering, Flinders University of SA, GPO Box 2100, Adelaide, SA 5001, Australia, He Fangpo , Sammut Karl
Published: April 2016

This paper highlights the theory of common-sense knowledge in terms of representation and reasoning. A connectionist model is proposed for common-sense knowledge representation and reasoning. A generic fuzzy neuron is used as a basic element for the connectionist model. The representation and reasoning ability of the model are described through examples. A common-sense knowledge base is employed to develop a human face detection system. The system consists of three stages: preprocessing, face-components extraction, and final decision making. A neural-network-based algorithm is utilised to extract face components. Five networks are trained to detect the mouth, nose, eyes, and full face. The detected face components and their corresponding possibility degrees enable the knowledge base to locate faces in the image and to generate a membership degree for the detected faces within the face class. The experimental results obtained using this method are presented.
Intelligent Computer Methods for Modeling of Manufacturing Processes and Human Intent
Authors: Horváth László ,   Department of Manufacturing Engineering, Rudas Imre J. ,   Department of Information Technology, Bánki Donát Polytechnic, H-1081 Budapest, Népszínház u. 8., Hungary
Published: April 2016

This paper presents a novel methodology for modeling manufacturing processes of mechanical parts. The aim was to develop a manufacturing process model that describes all possible process variants in a single model and involves generic process description for a cluster of manufacturing tasks. It must be fit into the product model concept. A four-level generic manufacturing process model has been developed by using Petri net representation for model entities. Advanced shape models do not describe the intent of the designer and other information that is necessary for the application of the model. As a contribution to solving this problem, we propose a methodology for attaching designer intent information and knowledge to geometric and form feature models. This improves communication between the product designer and production engineer. First, the importance of the manufacturing process model and its interconnections with other product related models are emphasized. Then, the structure, entities, creating, evaluation, and application of the manufacturing process model are explained. Next, product and production process modeling procedures are analyzed from the point of view of design intent information to be transferred between product designers and manufacturing engineers. Finally, characteristics of communication between engineers and modeling of human intent are outlined.
Fuzzy Reasoning with Tunable t-Operators
Authors: Vojtig Peter ,   Department of Computer Science, Faculty of Science, P. J. Safarik University Jesenna 5, 04154 Kosice, Slovak Republic
Published: April 2016

We introduce a model of fuzzy logic programming in a truth functional fuzzy logic with arbitrary and/or tunable t-operators. This t-operator tuning is the subject of different learning from neural networks to evolutionary calculation. The choice of an operator mostly depends on the real world problem modeled, often depending on user environments and/or stereotypes. To model aggregations of different witnesses, our rules have body in disjunctive normal form. We develop fuzzy fixpoint theory and show soundness and completeness of our semantics. To control calculational efficiency, we introduce a cut with threshold. For knowledge mining and tuning of the t-operator, we restrict the problem to finding a tnorm fitting finitely many values. We show that our model of fuzzy logic programs semantically coincides with a fuzzy controller model.
An Intelligent Automatic Surveillance System via Fuzzy Rule Base System and Genetic Algorithms
Authors: Kang Hyung-Jin ,   Intelligent Control and System Lab., Dept. of Electronic Eng., Yonsei University 134 Shinchon-dong, Seodaemun-gu, Seoul, 120-749, Korea, Lee Heejin , Noh Heung-Sik , Kim Jung-Hwan , Kim Dong-Yon , Park Mignon ,   Dept. of Electronic Eng., Ansung National University 67 Sukjung-dong, Ansung, Kyungki, 456-749, Korea
Published: April 2016

In this paper, an intelligent Automatic Surveillance System is proposed using fuzzy rule base system and genetic algorithms. The aim of our Automatic Surveillance System is to detect a moving object and make a decision on whether it is human or not. Various object features such as the ratio of the width and the length of the moving object, the distance dispersion between the principal axis and the object contour, the eigenvectors, the symmetric axes, and the areas of the segmented regions are used in this paper. These features are not unique and decisive characteristics for representing human. Also, due to the outdoor image property, the object feature information is unavoidably vague and inaccurate. In order to make an efficient decision from that information, we use a fuzzy rule base system as an approximate reasoning method The fuzzy rules, combining various object features, are able to describe the conditions for making an intelligent decision. The fuzzy rule base system is initially constructed by heuristic information and then, trained and tested with input/output data. For the effective training, the well-known Genetic Algorithms (GA) are used. Experimental results are shown, demonstrating the validity of our system.
Placement Time Optimization of Chip Mounter by Genetic Algorithms - Search for Optimal Tape Feeder Arrangement -
Authors: Miyajima Takashi ,   Industrial Research Institute of Nagano Prefecture, Koitabashi Tatsuo
Published: April 2016

Genetic algorithms (GAs) are applied to searching for tape feeder arrangement to optimize placement time of chip mounters where a combination of parts placement sequence and tape feeder arrangement affect placement time under constraints. Compared to general GAs, our improved tape feeder arrangement GA using a new genetic operator called rotation was confirmed effective for multihead chip mounters handling several parts at once.
Self-Organization and Adaptation in Intelligent Systems
Authors: Kasabov Nikola ,   Department of Information Science, University of Otago P.O Box 56, Dunedin, New Zealand, Kozma Robert ,   Division of Neurobiology, Dept. Molecular & Cell Biology LSA 129, University of California Berkeley Berkeley, CA 94720-3200
Published: April 2016

This special issue is devoted to one of the important topics of current intelligent information systems-their ability to adapt to the environment they operate in, as adaptation is one of the most important features of intelligence. Several milestones in the literature on adaptive systems mark the development in this area. The Hebbian learning rule,1) self-organizing maps,2,3) and adaptive resonance theory4) have influenced the research in this area a great deal. Some current development suggests methods for building adaptive neurofuzzy systems,5) and adaptive self-organizing systems based on principles from biological brains.6) The papers in this issue are organized as follows: The first two papers present material on organization and adaptation in the human brain. The third paper, by Kasabov, presents a novel approach to building open structured adaptive systems for on-line adaptation called evolving connectionist systems. The fourth paper by Kawahara and Saito suggests a method for building virtually connected adaptive cell structures. Papers 5 and 6 discuss the use of genetic algorithms and evolutionary computation for optimizing and adapting the structure of an intelligent system. The last two papers suggest methods for adaptive learning of a sequence of data in a feed-forward neural network that has a fixed structure. References: 1) D.O. Hebb, "The Organization of Behavior," Jwiley, New York, (1949). 2) T. Kohonen, "Self-organisation and associative memory," Springer-Verlag, Berlin, (1988). 3) T. Kohonen, "Self-Organizing Maps, second edition," Springer Verlag, (1997). 4) G. Carpenter and S. Grossberg, "Pattern recognition by self-organizing neural networks," The MIT Press, Cambridge, Massachusetts, (1991). 5) N. Kasabov, "Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering," The MIT Press, CA, MA, (1996). 6) S. Amari and N. Kasabov "Brain-like Computing and Intelligent Information Systems," Springer Verlag, Singapore, (1997).
A Novel Penalty Function Approach to Constrained Optimization Problems with Genetic Algorithms
Authors: Yu Xinghuo ,   Faculty of Informatics and Communication, Central Queensland University Rockhampton OLD 4702, Australia, Zheng Weixing , Wu Baolin , Yao Xin ,   School of Science, University of Western Sydney Nepean, NSW 2747, Australia,   School of Computer Science, University College, University of New South Wales ADFA, ACT 2600, Australia
Published: April 2016

In this paper, a novel penalty function approach is proposed for constrained optimization problems with linear and nonlinear constraints. It is shown that by using a mapping function to "wrap" up the constraints, a constrained optimization problem can be converted to an unconstrained optimization problem. It is also proved mathematically that the best solution of the converted unconstrained optimization problem will approach the best solution of the constrained optimization problem if the tuning parameter for the wrapping function approaches zero. A tailored genetic algorithm incorporating an adaptive tuning method is then used to search for the global optimal solutions of the converted unconstrained optimization problems. Four test examples were used to show the effectiveness of the approach.
Local Learning Algorithms for Sequential Tasks in Neural Networks
Authors: Robins Anthony ,   Computer Science, The University of Otago PO Box 56, Dunedin, New Zealand, Frean Marcus ,   Electrical and Computer Engineering, University of Queensland Brisbane 4072, Australia
Published: April 2016

In this paper, we explore the concept of sequential learning and the efficacy of global and local neural network learning algorithms on a sequential learning task. Pseudorehearsal, a method developed by Robins19) to solve the catastrophic forgetting problem which arises from the excessive plasticity of neural networks, is significantly more effective than other local learning algorithms for the sequential task. We further consider the concept of local learning and suggest that pseudorehearsal is so effective because it works directly at the level of the learned function, and not indirectly on the representation of the function within the network. We also briefly explore the effect of local learning on generalization within the task.
Extending Learning Feasibility Through Feedforward Sequential Learning
Authors: Weir Michael K. ,   Department of Mathematics and Computer Science, University of St Andrews St Andrews, U.K., Chen Li Hui ,   Division of Information Engineering, School of EEE, Nanyang Technological University Singapore.
Published: April 2016

In this paper, a sequence-based neural network approach called feedforward sequential learning (FSL) is proposed for extending the range of feasibility for feedforward networks in the three areas of architecture, training, and generalization. The extension is enabled through a spatio-temporal indexing scheme that decomposes the task into a sequence of simpler subproblems. Each subproblem is then solved by a separate weight state. The separate trained weight states are then combined into a continuous final weight state sequence to enable smooth generalization. FSL can be used to train mappings of analog or discrete I/O with underlying continuity for pattern association or classification. Implementation of FSL is illustrated and tested by learning the 2-spirals problem and an extended 4-spiral version. Training is found to be faster and more robust than its single-state counterpart. The generalization obtained indicates that the underlying patterns are classified more smoothly with FSL. Overall, the results suggest FSL to be a feasible approach to consider for complex and decomposable tasks.
An Adaptive Tabu Search (ATS) and Other Metaheuristics for a Class of Optimal Allocation Problems
Authors: Takahara Shigeyuki ,   Kagawa Prefectural Industrial Technology Center, Miyamoto Sadaaki ,   Institute of Information Sciences and Electronics, University of Tsukuba Ibaraki 305-8573, Japan
Published: April 2016

We propose an ATS, for a class of optimal allocation problems, that uses various types of memory to make the search effective. To improve efficiency, our tabu search (TS) uses a combination of objects, rather than moves, as memory elements, simplifying search constraints and improving search efficiency. The ATS is applied to optimal allocation of irregular shapes on a sheet to minimize waste. The effectiveness of the proposed method is compared to other metaheuristics in a simulation whose results show the ATS to be superior.
Evolutionary Strategy Using Statistical Information and Its Application to Mobile Robot Control
Authors: Izumi Kiyotaka ,   Department of Mechanical Engineering, Faculty of Science and Engineering,, Watanabe Keigo , Hashem M.M.A. ,   Department of Advanced Systems Control Engineering, Graduate School of Science and Engineering,,   Faculty of Engineering Systems and Technology, Graduate School of Science and Engineering, Saga University 1-Honjomachi, Saga 840-8502, Japan
Published: April 2016

We describe an evolution strategy (ES) using the statistical information of subgroups obtained automatically by a similarity metric of individuals at each generation. Arithmetical crossover is done with an elite individual and a mean individual within each subgroup to produce offspring. Standard deviation calculated within a subgroup is used in mutation. The effectiveness of the proposed ES is first shown with tests of the 5 De Jong functions. The present ES is also applied to the acquisition of control for a terminal control problem in an omnidirectional mobile robot, in which robot control is based on fuzzy behavior-based control that combines subsumption-like architecture and fuzzy reasoning.
Computational Intelligence for Robust Control Algorithms of Complex Dynamic Systems with Minimum Entropy Production
Authors: Ulyanov S.V. ,   Research & Development Office , Yamaha Motor Europe N.V., Polo Didattico e di Ricerca di Crema, Via Bramante, 65-26013 CREMA (CR) - Italy, Yamafuji K. , Ulyanov V.S. , Kurawaki I. , Hagiwara T. , Panfilov S.A. ,   Mechanical and Control Eng. Dept., University of Electro-Communications, 1-5-1, Chofugaoka, Chofu, Tokyo 182-8585, Japan
Published: April 2016

Our thermodynamic approach to the study and design of robust optimal control processes in nonlinear (in general global unstable) dynamic systems used soft computing based on genetic algorithms with a fitness function as minimum entropy production. Control objects were nonlinear dynamic systems involving essentially nonlinear stochastic differential equations. An algorithm was developed for calculating entropy production rate in control object motion and in control systems. Part 1 discusses relation of the Lyapunov function (measure of stability) and the entropy production rate (physical measure of controllability). This relation was used to describe the following qualitative properties and important relations: dynamic stability motion (Lyapunov function), Lyapunov exponent and Kolmogorov-Sinai entropy, physical entropy production rates, and symmetries group representation in essentially nonlinear systems as coupled oscillator models. Results of computer simulation are presented for entropy-like dynamic behavior for typical benchmarks of dynamic systems such as Van der Pol, Duffing, and Holmes-Rand, and coupled oscillators. Parts 2 and 3 discuss the application of this approach to simulation of dynamic entropy-like behavior and optimal benchmark control as a 2-link manipulator in a robot for service use and nonlinear systems under stochastic excitation.
Recursive Fuzzy Modeling Based on Fuzzy Interpolation
Authors: Shouraki Saeed Bagheri ,   Department of communication and systems, University of Electro-Communications Chofu-Shi, Tokyo 182-8285, Japan, Honda Nakaji
Published: April 2016

This paper introduces a new fuzzy modeling of an unknown system. The heart of the proposed modeling is fuzzy interpolation involving resolution reduction that generates two different types of information to define single-input, single-output subsystems for an unknown system. Input is identified using a heuristic based on the proposed technique. System behavior is defined as a curve corresponding to individual input. These curves are found using fuzzy curve fitting applied to data points. Behavior curves are saved one of three ways. When the first is geometrical, the second and third use a neural network as a linear prediction coder. Convergence of the linear prediction coder specifies the membership function domain. Inference rules are extracted by observing the exactness of single-input behavior using a fuzzy method. The combination rule is new and combines single-input, single-output behavior to obtain the system model. Our proposal cancels noise well, recognizes actual input, and expresses complicated nonlinear systems with a very small number of rules.
Fuzzy Adaptive Search Method for Genetic Programming
Authors: Maeda Yoichiro ,   Faculty of Information Science and Technology, Osaka Electro-Communication University 18-8, Hatsu-cho, Neyagawa, Osaka 572-8530, Japan
Published: April 2016

The greatest problem of genetic programming (GP) is the large amount of calculation. We propose two methods for an efficient GP search - RPGP reducing void searches by pruning redundant nodes in GP and FASGP enabling proper search by tuning a mutation and crossover rate by fuzzy rules. Simulation proved the effectiveness of our proposal.
Computational Intelligence with New Physical Controllability Measure for Robust Control Algorithm of Extension- Cableless Robotic Unicycle
Authors: Ulyanov V.S. ,   Department of Mechanical and Control Engineering, The University of Electro-Communications, 1-5-1Chofugaoka,Chofu, Tokyo 182-8585, Japan, Yamafuji K. , Ulyanov S.V. , Tanaka K. ,   Research & Development Office , Yamaha Motor Europe N.V., Polo Didattico e di Ricerca di Crema, Via Bramante, 65-26013 CREMA (CR) - Italy
Published: April 2016

The biomechanical robotic unicycle system uses internal world representation described by emotion, instinct, and intuition. The basic intelligent control concept for a complex nonlinear nonholonomic biomechanical systems, as benchmark the extension-cableless robotic unicycle, uses a thermodynamic approach to study optimum control processes in complex nonlinear dynamic systems is represented here. An algorithm for calculating the entropy production rate is developed. A new physical measure, the minimum entropy production rate, is used as a Genetic Algorithm (GA) fitness function to calculate robotic unicycle robustness controllability and intelligent behavior. The interrelation between the Lyapunov function - a measure of stochastic stability - and the entropy production rate - the physical measure of controllability - in the biomechanical model is the mathematical background for designing soft computing algorithms in intelligent robotic unicycle control. The principle of minimum entropy production rate in control systems and control object motion in general is a new physical concept of smart robust control for the complex nonlinear nonholonomic biomechanical system, as benchmark, extension-cableless robotic unicycle.
On the Optimization of Fuzzy-Controllers by Neural Networks
Authors: Lippe Wolfram-M. ,   Westfalische Wilhelms-Universitat Munster Institut fur Informatik EinsteinstraBe 62, 48149 Munster, Germany, Niendieck Steffen , Tenhagen Andreas
Published: April 2016

Methods are known for combining fuzzy-controllers with neural networks. One of the reasons of these combinations is to work around the fuzzy controllers’ disadvantage of not being adaptive. It is helpful to represent a given fuzzy controller by a neural network and to have rules adapted by a special learning algorithm. Some of these methods are applied in the NEFCONmode or the model of Lin and Lee. Unfortunately, none adapts all fuzzy-controller components. We suggest a new model enabling the user to represent a given fuzzy controller by a neural network and adapt its components as desired.
Fuzzy Geometry Database
Authors: Buckley James J. ,   Department of Mathematics, University of Alabama at Birmingham Birmingham, Alabama, 35294, USA, Feuring Thomas , Hayashi Yoichi ,   Department of Electrical Engineering and Computer Science, University of Siegen HSlderlinstr. 3, 57068 Siegen, Germany,   Department of Computer Science, Meiji University 1-1-1 Higashimita, Tama-ku, Kawasaki, 214-8571, Japan
Published: April 2016

We suggest using elements from fuzzy geometry to form queries into both crisp and fuzzy databases. Numerous examples of such queries are presented and we give numerical results for a crisp database. We show in the fuzzy database that queries and most responses are in terms of linguistic variables. , a query is of the form "what is near (around 40 years old and income approximately $75,000) " with response "(around 50 years old and rich), (old and income approximately $90,000),...".
Neuro-Fuzzy Systems Approaches
Authors: Rutkowska Danuta ,   Department of Computer Engineering, Technical University of Czestochowa Armii Krajowej 36, 42-200 Czestochowa, Poland, Hayashi Yoichi ,   Department of Computer Science, Meiji University 1-1-1 Higashimita, Tama-ku, Kawasaki, 214-8571, Japan
Published: April 2016

Two major approaches to neuro-fuzzy systems are distinguished in the paper. The previous one refers to fuzzy neural networks, which are neural networks with fuzzy signals, and/or fuzzy weights, as well as fuzzy transfer functions. The latter approach concerns neuro-fuzzy systems in the form of multilayer feed-forward networks, which differ from standard neural networks, because elements of particular layers conduct different operations than standard neurons. These structures are neural network representations of fuzzy systems and they are also called connectionist models of fuzzy systems, adaptive fuzzy systems, fuzzy inference neural networks, etc. Two different defuzzifiers, applied to fuzzy systems, are in focus of the paper. Center-of-sums method is an example of parametric defuzzification. Standard neural networks a defuzzifier presents nonparametric approach to defuzzification. For both cases learning algorithms of neuro-fuzzy systems are proposed. These algorithms take a form of recursions derived based on the momentum back-propagation method. Computer simulation demonstrates a comparison between performance of neuro-fuzzy systems with the parametric and nonparametric defuzzifier. Truck backer-upper control problem has been used to illustrate the systems performance. Conclusions concerning the simulation results are summarized. The paper pertains many references on neuro-fuzzy systems, especially selected publications of Czogala, whom it is dedicated.
Self-Tuning for Fuzzy Rule Generation Based upon Fuzzy Singleton-type Reasoning Method
Authors: Shi Yan ,   School of Engineering, Kyushu Tokai University 9-1-1, Toroku, Kumamoto 862-8652, Japan, Mizumoto Masaharu ,   Division of Information and Computer Sciences Osaka Electro-Communication University Neyagawa, Osaka 572-8530, Japan
Published: April 2016

Using fuzzy singleton-type reasoning method, we propose a self-tuning method for fuzzy rule generation. We give a neurofuzzy learning algorithm for tuning fuzzy rules under fuzzy singleton-type reasoning method, then roughly design initial tuning parameters of fuzzy rules based on a fuzzy clustering algorithm before learning a fuzzy model. This should reduce learning time and fuzzy rules generated by our approach are reasonable and suitable for the identified model. We demonstrate our proposal’s efficiency by identifying nonlinear functions.
Utility Revision Mechanism Based on User’s Subjective Decision Hierarchy for Multiagent-Based Group Decision Support
Authors: Ito Takayuki ,   Department of Intelligence and Computer Science Nagoya Institute of Technology Gokiso, Showa-ku, Nagoya 466-8555, Japan, Shintani Toramatsu
Published: April 2016

We propose a utility revision mechanism for persuasion among agents in the Group Choice Design Support System (GCDSS). GCDSS is a group decision support system based on multiagent negotiation. In GCDSS, each user manages a system for an Analytic Hierarchy Process (AHP) and an agent. Each user subjectively constructs a decision hierarchy and determines weights of alternatives using AHP Based on the hierarchy and weights, agents negotiate with each other on behalf of users. In negotiation, agents attempt to persuade each other. If an agent is persuaded, the agent attempts to revise its utility for a state of its decision hierarchy by adjusting pairwise comparisons in AHP We propose a utility revision mechanism for persuasion among agents using the user’s subjective decision hierarchy of AHP to make persuasion more flexible. Agents try to make their decision hierarchy reliable to revise its utility.
Theories of Set and Logic with Crisp or Fuzzy Information Granules
Authors: Turksen I.B. ,   Department of Mechanical and Industrial Engineering University of Toronto Toronto, Ontario M5S 3G8 Canada
Published: April 2016

Four theories may be generated with the combination of information granules for sets and logics. First a descriptive set membership value may be assigned to be {0,1} or [0,1] in an information granule that describes a set with a relational constraint; and secondly, a logical assertion set membership value may be assigned to be {0,1} or [0,1] in an information granule that states a veristic constraint associated with the given set description stated in the first case. These two types of assignments must be first distinguished from each other and then combined for a new construction of the combined theories of sets and logics. In this discourse, therefore, two types of information granules are considered: (i) one for descriptive set assignments and (ii) the other for truthood assignments. The combination of these two distinct assignments let us generate four set and logic theories. This then leads to the concern of canonical forms and their derivation from truth tables for each of these theories. In this regard, some of the fundamental issues arising in this context are discussed and certain preliminary answers are provided in order to highlight the consequences of these theories.
Control of Decentralized Systems Based on Nash Equilibrium Concept of Game Theory
Authors: Hirasawa Kotaro ,   Department of Electrical and Electronic Systems Engineering, Kyushu University, Hu Jinglu , Yamamoto Yusuke , Jin Chunzhi , Eki Yurio ,   Hitachi Co. Ltd., Omika, Hitachi City, Jbaraki, Japan
Published: April 2016

In this paper, a new control method for decentralized systems is proposed by using the concept of Nash equilibrium points of non co-operative game. The main reason for applying non co-operative game to decentralized systems is that multi-objective decentralized systems can be easily formulated without considering weighting factor of each objective function. It is supposed that the system stated in this article is composed of many subsystems including their own controllers, so each subsystem can be regarded as a player in the game. Each subsystem and its controller are described by the Universal Learning Networks which have been proposed to provide a universal framework for the class of neural networks. From the above assumptions it is theoretically shown that if the criterion function for each subsystem can be defined individually, the Nash equilibrium points can be calculated by the commonly used gradient learning algorithm of networks. Simulation studies on a decentralized tank network control system show that the Nash equilibrium points can be obtained systematically and effectively by the proposed method.
Moving Genetic Algorithm Based Fuzzy Modeling
Authors: Kim Euntai ,   Department of Control and Instrumentation Engineer Hankyong National University 67 Sukjung-dong, Ansung, Kyungki, 456-749, Korea, Lee Heejin , Lee Chang-Hoon , Kim Jung-Hwan ,   Department of Electronic Enginerring Yonsei University
Published: April 2016

We propose an approach to Takagi-Sugeno fuzzy modeling via a genetic algorithm consisting of 2 tuning steps - coarse and fine. A moving genetic algorithm (MGA) is proposed and used for fine tuning to obtain robust modeling results. Simulation results demonstrate the algorithm’s validity.
Fuzzy Modeling with Local and Global Objectives
Authors: Yen John ,   Center for Fuzzy Logic, Robotics, and Intelligent Systems, Gillespie Wayne ,   Department of Computer Science Texas A&M University College Station, Texas 77843-3112
Published: April 2016

Most of the techniques for constructing fuzzy models from data focus only on minimizing the error between the model’s output and the training data; however, these approaches may result in a fuzzy model where individual rules are misleading. The goal of our research is to develop a scheme for identifying Takagi-Sugeno-Kang (TSK) models whose individual rules approximate the training data covered by a single rule, local fitness, while the entire model approximates the whole training set, global fitness. We propose an approach that is a modification of a current method for estimating the consequence portion of a TSK model with predefined membership functions. Then we propose a method for developing membership functions which partition the input space into regions that are more easily modeled in the TSK framework to provide consistent local behavior for all the rules of the model. This approach ensures that a TSK model constructed not only approximates the input-output mapping relationship in the data, but also captures insights about the local behavior of the model.
Coevolutionary Algorithms for Realization of Intelligent Systems
Authors: Jun Hyo-Byung ,   Robotics and Intelligent Information System Laboratory School of Electrical and Electronic Engineering Chung-Ang University 221, Huksuk-Dong, Dongjak-Ku, Seou1156-756, Korea, Sim Kwee-Bo
Published: April 2016

The simple genetic algorithm (SGA) proposed by J. H. Holland uses population-based optimization based on Darwinian natural selection. The theoretical foundations of GA are the Schema Theorem and Building Block Hypothesis. Although GA does well in many applications in optimization, it does not guarantee the convergence to a global optimum in some problems. In designing intelligent systems, since there is no deterministic solution, heuristic trial-and error is usually used to determine system parameters. An alternative is a coevolutionary system, where 2 populations constantly interact and coevolve. We review coevolutionary algorithms and propose coevolutionary schemes designing intelligent systems based on the relationship between system components.
Design of Self-Learning Hierarchical Fuzzy Logic for Guidance and Control of Multirobot Systems
Authors: Mohammadian Masoud ,   School of Computing, University of Canberra Canberra, ACT, Australia
Published: April 2016

Increased application of fuzzy logic to complex control raises a need for a structured methodological approach to developing fuzzy logic systems, which are currently developed based on individualistic bases and cannot face the challenge of interacting with other (fuzzy) systems in a dynamic environment. We propose designing self-learning hierarchical fuzzy logic control based on the integration of evolutionary algorithms and fuzzy logic to provide an integrated knowledge base for intelligent control and collision avoidance among multiple robots. Robots are considered point masses moving in common work space. Evolutionary algorithms are used as an adaptive method for learning the fuzzy knowledge base of control systems and learning, mapping, and interaction between fuzzy knowledge bases of different fuzzy logic systems.
Speaker Verification with Fuzzy Fusion and Genetic Optimization
Authors: Pham Tuan ,   Institute of Information Sciences and Technology, Massey University Palmerston North, New Zealand, Wagner Michael ,   Faculty of Information Sciences and Engineering, University of Canberra ACT 2601, Australia
Published: April 2016

Most speaker verification systems are based on similarity or likelihood normalization techniques as they help to better cope with speaker variability. In the conventional normalization, the it a priori probabilities of the cohort speakers are assumed to be equal. From this standpoint, we apply the fuzzy integral and genetic algorithms to combine the likelihood values of the cohort speakers in which the assumption of equal a priori probabilities is relaxed. This approach replaces the conventional normalization term by the fuzzy integral which acts as a non-linear fusion of the similarity measures of an utterance assigned to the cohort speakers. Furthermore, genetic algorithms are applied to find optimal fuzzy densities which are very important for the fuzzy fusion. We illustrate the performance of the proposed approach by testing the speaker verification system with both the conventional and the proposed algorithms using the commercial speech corpus TI46. The results in terms of the equal error rates show that the speaker verification system using the fuzzy integral is more favorable than the conventional normalization method.
Realization of PID Control by Fuzzy Inference and its Application to Hybrid Control
Authors: Hayashi Kenichiro ,   Applied Electronics Division, Industrial Technology Center of Saga 114 Yaemizo, Nabeshima-cho, Saga-shi 849-0932, Japan Tel. +81-952-30-8161Fax: +81-952-25-1694, Otsubo Akifumi , Shiranita Kazuhiko
Published: April 2016

First, we propose a more practical method for realizing nonlinear and linear PID control by fuzzy inference. By applying the simplified direct inference of fuzzy inference to only a few simple fuzzy control rules, both nonlinear PID control based on a conventional PID controller and linear PID control are more simply realized than by previous methods. Next, we propose a hybrid control method in which fuzzy and PID control are hybridized for mutual compensation of both types of control. In this proposal, fuzzy control (nonlinear PID control) and PID control (linear PID control), realized by the simplified direct inference of fuzzy inference, are combined in parallel and used alternately. The main characteristics of this method are high-speed control realized by applying the simplified direct inference of fuzzy inference and smoothness of switching control guaranteed by fuzzy switching of fuzzy and PID control.
Narrowing Algorithm for Indoor-Air Pollutants using Gas Sensor Patterns
Authors: Oyabu Takashi ,   Kanazawa University of Economics Kanazawa 920-8620, Japan, Onodera Takeshi , Nambo Hidetaka , Kimura Haruhiko ,   Kanazawa University, Faculty of Engineering Kanazawa 920-8667, Japan
Published: April 2016

There are many types of gaseous indoor-air pollutants in the domestic environment, generally mixtures of several gases. These gases harm people in houses with sick house syndrome. It is important to identify pollutants and their grades. We narrow many possible pollutants to two or three using output patterns of plural tin-oxide gas sensors. Six types of sensors are used in the system. The algorithm to identify the mixture of gas is made using the characteristics of sensor patterns and has high reliability, especially in detecting alcoholic gas, carbon dioxide, and carbon monoxide gases. It consists of several general steps and sensitivity grade of each sensor in a gas mixture is important to identify polluting gases. In practice, OPS5, an expert system, is used to narrow the types of gas.
Visualization of Categorical Data by Hybridization of Two Types of Neural Networks
Authors: Tanaka Masahiro ,   Faculty of Science, Konan University 8-9-1 Okamoto, Higashinada-ku, Kobe 658-8501, Japan, Fujiwara Hideki ,   Sakaide Plant, Mitsubishi Chemical Corporation Bannosu-cho, Sakaide, Kagawa 762-8510, Japan
Published: April 2016

The sandglass neural network is often used for nonlinear auto-association, where the principal information can be extracted by picking up the values of the middle layer. However, the boundary of the classes on this 2-1) surface tends to be complicated because no class information is used. In this paper, the hybridization of auto-associative network and the multi-layer perceptron for classification is proposed. The usefulness of this method is demonstrated by using clinical data.
Fast Neuro-Classification of New and Used Bills Using Spectral Patterns of Acoustic Data
Authors: Kang Dongshik ,   College of Engineering, Osaka Prefecture University 1-1 Gakuen-cho, Sakai, Osaka 599-8531, Japan, Omatu Sigeru , Yoshioka Michifumi
Published: April 2016

An advanced neuro-classification of new and used bills using the spectral patterns is proposed. An acoustic spectral pattern is obtained from the output of the two-stage adaptive digital filters (ADFs) for time-series acoustic data. The acoustic spectral patterns are fed to a competitive neural network, and classified into some categories which show worn-out degrees of the bill. The proposed method is based on extension of an ADF, an individual adaptation (IA) algorithm, and a learning vector quantization (LVQ) algorithm. The experimental results show that the proposed method is useful to classify new and used bills.
The Application of Hybrid Evolving Connectionist Systems to Image Classification
Authors: Kasabov Nikola K. ,   Department of Information Science, Israel Steven A. , Woodford Brendon J. ,   Department of Surveying University of Otago PO Box 56 Dunedin New Zealand
Published: April 2016

This paper presents a methodology for image classification of both spatial and spectral data with the use of hybrid evolving fuzzy neural networks (EFuNNS). EFuNNs are five layer sparsely connected networks. EFuNNs contain dynamic structures that evolve by growing and pruning of neurons and connections. EFuNNS merge three supervised classification methods: connectionism, fuzzy logic, and case-based reasoning. By merging these strategies, this new structure is capable of learning and generalising from a small sample set of large attribute vectors as well as from large sample sets and small feature vectors. Two case studies data are used to demonstrate the effectiveness of the methodology. First, an environmental remote sensing application, and second, large scale images of fruit for automated grading. The proposed methodology provides fast and accurate adaptive learning for image classification. It is also applicable for on-line, real-time learning and classification.
Fuzzy Rule Interpolation by the Conservation of Relative Fuzziness
Authors: Koczy Laszlo T. ,   International Education Center, Budapest University of Technology and Economics Building Z. 1F. 102, Bertalan L str 2, Budapest, H-1111, Hungary, Hirota Kaoru , Gedeon Tamas D. ,   Department of Computational Intelligence and Systems Sciences Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology 4259 Nagatsuta, Midori-ku, Yokohama 226, Japan,   School of Information Technology, Murdoch University Perth WA, 6150, Australia
Published: April 2016

If the number of variables is growing the size of fuzzy rule bases increase exponentially. To reduce size and inference/control time, it is often necessary to deal with sparse rule bases. In such bases, classic algorithms such as the CRI of Zadeh and the Mamdani-method do not function. In such rule bases, rule interpolation techniques are necessary. The linear rule interpolation (KHinterpolation) based on the Fundamental Equation of Interpolation introduced by Koczy and Hirota is suitable for dealing with sparse bases, but this method often results in conclusions which are not directly interpretable, and need some further transformations. One of the possible ways to avoid this problem is the interpolation method based on the conservation of fuzziness, proposed recently by Gedeon and Koczy for trapezoidal fuzzy sets. In this paper, a refined version of that method will be presented that is fully in accordance with the Fundamental Equation, with extensions to multiple dimensions, and then to arbitrarily shaped membership functions. Several possibilities for the latter will be shown.
Application of Genetic Algorithm and K-Nearest Neighbour Method in Real World Medical Fraud Detection Problem
Authors: He Hongxing ,   Health Insurance Commission 134 Reed Street, P O. Box 1001 Tuggeranong, ACT 2900, Australia, Hawkins Simon , Graco Warwick , Yao Xin ,   CRC for Advanced Computational Systems, CSIRO Mathematical and Information Sciences GPO Box 664, Canberra, ACT 2601 Australia,   School of Computer Science, University of Birmingham Birmingham B15 2TT, United Kingdom
Published: April 2016

In the k-Nearest Neighbour (kNN) algorithm, the classification of a new sample is determined by the class of its k nearest neighbours. The performance of the kNN algorithm is influenced by three main factors: (1) the distance metric used to locate the nearest neighbours; (2) the decision rule used to derive a classification from the k-nearest neighbours; and (3) the number of neighbours used to classify the new sample. Using k = 1, 3, or 5 nearest neighbours, this study uses a Genetic Algorithm (GA) to find the optimal non-Euclidean distance metric in the kNN algorithm and examines two alternative methods (Majority Rule and Bayes Rule) to derive a classification from the k nearest neighbours. This modified algorithm was evaluated on two real-world medical fraud problems. The General Practitioner (GP) database is a 2-class problem in which GPs are classified as either practising appropriately or inappropriately. The ’.Doctor-Shoppers’ database is a 5-class problem in which patients are classified according to the likelihood that they are ’doctor-shoppers’. Doctor-shoppers are patients who consult many physicians in order to obtain multiple prescriptions of drugs of addiction in excess of their own therapeutic need. In both applications, classification accuracy was improved by optimising the distance metric in the kNN algorithm. The agreement rate on the GP dataset improved from around 70% (using Euclidean distance) to 78 % (using an optimised distance metric), and from about 55% to 82% on the Doctor Shopper’s dataset. Differences in either the decision rule or the number of nearest neighbours had little or no impact on the classification performance of the kNN algorithm. The excellent performance of the kNN algorithm when the distance metric is optimised using a genetic algorithm paves the way for its application in the real world fraud detection problems faced by the Health Insurance Commission (HIC).
An Adaptive Penalty Function Method for Constrained Optimization with Evolutionary Programming
Authors: Yu Xinghuo ,   Faculty of Informatics and Communication Central Queensland University Rockhampton Qld 4702, Australia, Wu Baolin
Published: April 2016

In this paper, we propose a novel adaptive penalty function method for constrained optimization problems using the evolutionary programming technique. This method incorporates an adaptive tuning algorithm that adjusts the penalty parameters according to the population landscape so that it allows fast escape from a local optimum and quick convergence toward a global optimum. The method is simple and computationally effective in the sense that only very few penalty parameters are needed for tuning. Simulation results of five well-known benchmark problems are presented to show the performance of the proposed method.
Application of Fuzzy Control to a Riderless Bicycle
Authors: Lachiver Gérard ,   Department of Electrical Engineering and Computer Engineering, Faculty of Engineering, Universite de Sherbrooke 2500, Blvd de 1’Université, Sherbrooke, Que., Canada HK 2R1, Berriah Saïd
Published: April 2016

A scale model of a bicycle equipped with a stabilization system was developed in the Mechatronics laboratory of the Department of Electrical Engineering and Computer Engineering of the Universite de Sherbrooke. Firstly, a proportional integral automatic control with adjusted gain was developed to make possible the riding of a bicycle using a remote control. Secondly, an intelligent control architecture based on a fuzzy controller was developed to make the bicycle stable and duplicate a human rider.