Showing papers on "Soft computing published in 1997"

Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review]

Abstract: Interestingly, neuro fuzzy and soft computing a computational approach to learning and machine intelligence that you really wait for now is coming. It's significant to wait for the representative and beneficial books to read. Every book that is provided in better way and utterance will be expected by many peoples. Even you are a good reader or not, feeling to read this book will always appear when you find it. But, when you feel hard to find it as yours, what to do? Borrow to your friends and don't know when to give back it to her or him.

Fuzzy and neural approaches in engineering

Abstract: From the Publisher: Fuzzy and Neural Approaches in Engineering presents a detailed examination of the fundamentals of fuzzy systems and neural networks and then joins them synergistically - combining the feature extraction and modeling capabilities of the neural network with the representation capabilities of fuzzy systems. Exploring the value of relating genetic algorithms and expert systems to fuzzy and neural technologies, this forward-thinking text highlights an entire range of dynamic possibilities within soft computing. With examples of specifically designed to illuminate key concepts and overcome the obstacles of notation and overly mathematical presentations often encountered in other sources, plus tables, figures, and an up-to-date bibliography, this unique work is both an important reference and a practical guide to neural networks and fuzzy systems.

Automatic Learning Techniques in Power Systems

Abstract: From the Publisher: Automatic Learning Techniques in Power Systems is dedicated to the practical application of automatic learning to power systems Power systems to which automatic learning can be applied are screened and the complementary aspects of automatic learning, with respect to analytical methods and numerical simulation, are investigated This book presents a representative subset of automatic learning methods - basic and more sophisticated ones - available from statistics (both classical and modern), and from artificial intelligence (both hard and soft computing) The text also discusses appropriate methodologies for combining these methods to make the best use of available data in the context of real-life problems Automatic Learning Techniques in Power Systems is a useful reference source for professionals and researchers developing automatic learning systems in the electrical power field

Soft computing: the convergence of emerging reasoning technologies

Abstract: The term Soft Computing (SC) represents the combination of emerging problem-solving technologies such as Fuzzy Logic (FL), Probabilistic Reasoning (PR), Neural Networks (NNs), and Genetic Algorithms (GAs). Each of these technologies provide us with complementary reasoning and searching methods to solve complex, real-world problems. After a brief description of each of these technologies, we will analyze some of their most useful combinations, such as the use of FL to control GAs and NNs parameters; the application of GAs to evolve NNs (topologies or weights) or to tune FL controllers; and the implementation of FL controllers as NNs tuned by backpropagation-type algorithms.

Fuzzy Logic and Expert Systems Applications

Abstract: Ishibuchi, Fuzzy Neural Networks and their Applications. Chak, Feng, and Palaniswami, Implementation of Fuzzy Systems. Aiello, Burattini, and Tamburrini, Neural Networks and Rule-Based Systems. Fletcher andHinde, Construction of Rule Based Intelligent Systems. Pal and Mitra, Expert Systems in Soft Computing Paradigm. Watanabe and Tzafestas, Mean-Value-Based Functional Reasoning Techniques in the Development of Fuzzy-Neural Network Control Systems. Chen and Teng, Fuzzy Neural Network Systems in Model Reference Control Systems. Juditsky, Zhang, Delyon, Glorennec, and Benveniste, Wavelets in Identification.

Soft Computing in Engineering Design and Manufacturing

Abstract: From the Publisher: This book is a collection of some 47 research papers that were presented in June 1997 at the 2nd Online World Conference in Soft Computing. It covers the state-of-the-art techniques and applications of soft computing which will stimulate further advances towards the next generation of intelligent machines. Soft Computing in Engineering Design and Manufacturing will be of interest to graduate students and researchers involved in soft computing. It will also be useful for those working in related industrial environments.

The roles of fuzzy logic and soft computing in the conception, design and deployment of intelligent systems

Abstract: The essence of soft computing is that, unlike the traditional, hard computing, it is aimed at an accommodation with the pervasive imprecision of the real world Thus, the guiding principle of soft computing is: ‘exploit the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness, low solution cost and better rapport with reality’ In the final analysis, the role model for soft computing is the human mind

Software Agents and Soft Computing: Towards Enhancing Machine Intelligence, Concepts and Applications

Abstract: An introduction to agent technology.- Artificial societies and psychological agents.- Co-ordination in multi-agent systems.- Software agent technologies.- Information agents for the World Wide Web.- Multi-agent matchmaking.- An application of social filtering to movie recommendation.- Agents of change in business process management.- Agents, mobility and multimedia information.- A real-life experiment in creating an agent marketplace.- The roles of fuzzy logic and soft computing in the conception, design and deployment of intelligent systems.- An introduction to soft computing - A tool for building intelligent systems.- Basic concepts of a fuzzy logic data browser with applications.- Towards soft computing.- The rise of machine intelligence.- Intelligent software systems.- Machine intelligibility and the duality principle.

Software Agents and Soft Computing Towards Enhancing Machine Intelligence

Abstract: An introduction to agent technology.- Artificial societies and psychological agents.- Co-ordination in multi-agent systems.- Software agent technologies.- Information agents for the World Wide Web.- Multi-agent matchmaking.- An application of social filtering to movie recommendation.- Agents of change in business process management.- Agents, mobility and multimedia information.- A real-life experiment in creating an agent marketplace.- The roles of fuzzy logic and soft computing in the conception, design and deployment of intelligent systems.- An introduction to soft computing - A tool for building intelligent systems.- Basic concepts of a fuzzy logic data browser with applications.- Towards soft computing.- The rise of machine intelligence.- Intelligent software systems.- Machine intelligibility and the duality principle.

On the combination of fuzzy logic and evolutionary computation: a short review and bibliography

Abstract: In this paper, an evaluation of the current situation regarding a combination of fuzzy logic and evolutionary computation (EC) is given. This is made by classifying EC in fourteen areas, giving a short introduction to each of them, and presenting a selected bibliography (mainly journal contributions and book’s chapters) in these areas. The overview of this bibliography is, of course (and unfortunately), not complete, but should give a representative account of current focuses of research in the bidirectional integration of fuzzy logic and evolutionary computation.

Soft Computing Based Signal Prediction, Restoration, and Filtering

Abstract: In this chapter, soft computational signal processing, especially devoted to prediction, restoration and filtering of signals, is discussed. The neo-fuzzy-neuron, developed by the authors, are applied to the prediction and restoration of damaged signals. The chaotic signals and the speech signals are employed for the experiments. The filtering of noisy signals based on the Radial Basis Function (RBF) network, a special class of a fuzzy neural network, is also discussed. The proposed filter can eliminate not only Gaussian noise but also noise with an arbitrary distribution.

Sensor fusion in biomedical systems

Abstract: A universal system for evaluation of sensor fusion algorithms has been developed and validated with biomedical data. The implemented algorithms perform the association, correlation, and combination of information from single and multiple sensors and provide the capability to design and simulate the process of multisensor integration. Additional soft computing components enable the system to operate on symbolic, logical and numerical data. For hardware-in-the-loop tests the program is able to acquire data by analog-to-digital converters and an universal digital interface.

Pseudo-analysis as a mathematical base for soft computing

Abstract: There is presented a mathematical background under the name pseudo-analysis for treating problems with uncertainty, nonlinearity and optimization in soft computing. There are given some significant examples of applications in fuzzy logics, fuzzy sets, optimization, decision theory, fuzzy neural networks, parallel processing and control theory by Hamilton–Jacobi equations.

The roles of soft computing and fuzzy logic in the conception, design and deployment of intelligent systems

Abstract: Summary form only given. Soft computing (SC) is a consortium of methodologies which provide a foundation for intelligent systems. The principal methods are fuzzy logic (FL), neurocomputing (NC), genetic computing (GC) and probabilistic computing (PC), with PC subsuming evidential reasoning, uncertainty management and some machine learning theory. The main contribution of FL is a methodology for dealing with imprecision, approximate reasoning, fuzzy information granulation and computing with words; that of NC system identification, learning and adaption; that of CC systematized random research, tuning and optimization; and that of PC decision analysis and uncertainty management. The guiding principle of soft computing is: Exploit the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness, low solution cost and better rapport with reality. The 4 methods are complementary rather than competitive. Their use in combination leads to hybrid intelligent systems. The most visible of such systems are neuro-fuzzy systems. The ubiquity of intelligent systems is certain to have a profound impact on the ways in which man-made systems are conceived, designed, manufactured, employed and interacted with. This is the perspective in which basic issues relating to soft computing and intelligent systems are addressed.

On the numerical solutions of stochastic optimization problem

Abstract: In this paper we discuss the role of soft computing (SC) in optimization problems. We point out that SC is closely related to computational intelligence. Each aims at complementing the limitations of many conventional techniques.

Soft Computing Techniques in Knowledge-Based Intelligent Engineering Systems: Approaches and Applications

Abstract: Knowledge-based Intelligent Systems. Neural Network Paradigms. Fuzzy Logic in Engineering. Introduction to Evolutionary Computing Techniques Developing Knowledge-based Applications in Engineering. Real-Time Knowledge-based Systems: Concepts, Issues, Approaches. Analogue/Digital Circuits Representation for Design and Trouble Shooting in Intelligent Environment. Applications of Neural Networks in Engineering. Evolution of Neural Structures Based on Cellular Automata. Further Applications of ART Paradigms. Fuzzy Control - Design and Engineering Applications.

Applying fuzzy logic to codesign partitioning

Abstract: We propose a tool that will allow designers using the codesign approach to partially automate the development of embedded systems. The framework takes advantage of tools already available on the market for VLSI CAD as well as soft computing techniques. We focus our work mainly on evaluation of cost and partitioning, because this is the area in which soft computing seems to have great advantages over traditional approaches. The main novelty of our approach is our use of artificial intelligence techniques to imitate the behavior of a human in defining a system's partitioning. We hope to devote further studies to techniques to optimize the genetic algorithm, in both the representation and processing of data. We are also working on the use of formal techniques to describe our system.

Kolmogorov's theorem and its impact on soft computing

Abstract: In this chapter, we describe various applications of the Kolmogorov’s theorem on representing continuous functions of several variables (as superpositions of functions of one and two variables) to soft computing. Kolmogorov’s theorem leads to a theoretical justification, as well as to design methodologies, for neural networks. In the design of intelligent systems, Kolmogorov’s theorem is used to show that general logical operators can be expressed in terms of basic fuzzy logic operations.

An Introduction to Soft Computing - A Tool for Building Intelligent Systems

Abstract: “The essence of soft computing is that unlike the traditional, hard computing, soft computing is aimed at an accommodation with the pervasive imprecision of the real world. Thus, the guiding principle of soft computing is: ‘...exploit the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness, low solution cost and better rapport with reality’. In the final analysis, the role model for soft computing is the human mind.” [1]

On hardware support for interval computations and for soft computing: theorems

Abstract: This paper presents a rationale for providing hardware supported functions of more than two variables for processing incomplete knowledge and fuzzy knowledge. The result is in contrast to Kolmogorov's (1957) theorem in the numerical (nonfuzzy) case.

An Advanced Case-Knowledge Architecture Based on Fuzzy Objects

Abstract: The case-based reasoning (CBR) architecture described in this paper represents a substantive advancement in the representation of case-knowledge. It addresses three major problems found in current CBR schemes: 1) Insufficient treatment of abstract case features‘ context-dependent characteristics. 2) Lack of a methodical support for atomic and structured case features that contain and represent imprecisely specified quantities. 3) And little account for clustering and organising cognate cases into conceptually overlapping categories. To overcome the representational inadequacy resulting from those deficiencies, this work proposes two modelling fundamentals, namely, fuzzy primitive and fuzzy complex abstract features. These allow a flexible, polymorphic encoding of case characteristics as real numbers, linguistic terms, fuzzy numbers and fuzzy complex objects respectively. Based on this concept, it is possible to systematically organise a case base in fuzzy categories, reflecting real-world case clusters. In the presented scheme, a prototype case and its associated approximation scales form the basis to realise a versatile mechanism to represent the context-specific idiosyncrasies of fuzzy abstract case features. Case categories, fuzzy abstract features, cases, and the approximation scale concept are modelled as self-contained, operational entities. They co-operatively concert their services when they categorise an unclassified problem description (target case), and locate relevant stored cases. Applied to the Coronary Heart Disease risk assessment domain, the proposed architecture has proven to be highly adequate for capturing and efficiently processing case-knowledge. Moreover, as this scheme is designed upon well-established object-oriented principles, it has been shown that it can seamlessly integrate in a wider, more general knowledge management regime.

Soft computing and fuzzy logic

Abstract: What is soft computing? What is fuzzy computing? What is the relationship between them? This paper intends to provide clear answers to these questions. We focus on exploring the notions of the fuzzy coordinate system and the related transformations between qualitative and quantitative information. These notions are considered to be the core ideas of fuzzy computing. Then the three novel theories of fuzzy computing and soft computing developed by the first author of this paper, namely, the Falling Shadow Representation of fuzzy theory, the Factors Space theory and the Truth Value Flow Inference theory are introduced.

Fuzzy Controllers: An Integrated Approach Based on Fuzzy Logic, Rough Sets, and Evolutionary Computing

Abstract: There are two approaches to control theory. One is the classical hard computing approach. Its modern theory is based on differential geometry and topology. Another is fuzzy logic, a soft computing approach. Taking the views of both hard and soft computing, an integrated approach is proposed. The mathematical formalism for such an integrated structure is called the rough logic government. Intuitively, fuzzylogic is viewed as a methodology of constructing functions by a grand scale interpolation guided by qualitative information. Model theory of rough logic system is used to formalized the design of classical fuzzy logic controllers. The design is formulated as a sequence of transformations of mathematical models of a control system. It starts from a symbolic model that consists of predicates or propositions in rough logic. Such a model is referred to as a theory in formal logic. By experts’ suggestion, called fuzzificaton, t he symbolic model can be transformed into a fuzzy model that usually consists of rules of fuzzy sets (membership functions). In formal logic, such a transformation is called an interpretation of the theory. Of course, interpretations are usually not unique. The collection of all such interpretations is a highly structured set of membership functions; it is called a fibre space by differential geometers and topologists. Using one of the usual inference methods, the cross-sections of the fibre space is transformed into a “virtual” space of trajectories or integral submanifold of a differentiable manifold. Conceptually, some of these “virtual” trajectories or integral submanifolds should correspond to some solutions of the system equations of a classical dynamic system. By veification and validation, part of the “virtual” space solidifies into a “real” space of trajectories or integral submanifolds of a differentiable manifold. These “real” trajectories or integral submanifolds are solutions of the system equations of a classical dynamic system; of course, in fuzzy logic design, these equations are usually not explicitly constructed. Several new applications are identified, most notably one is the stability problem.

Soft computing algorithms for intelligent control of a mobile robot for service use: Part 1: Direct human-robot communications and managing system for cooperative control

Abstract: The arrangement principles and design methodology on soft computing for complex control framework of AI control system are introduced. The basis of this methodology is computer simulation of dynamics for mechanical robotic system with the help of qualitative physics and search for possible solutions by genetic algorithms (GA). New approach for direct human-robot communication with natural language (NL) and cognitive graphics is introduced. Active adaptation block which helps to mobile robot to learn a new actions and scripts based on soft computing as fuzzy neural networks, fuzzy control and genetic algorithms are proposed.

Soft Computing Explains Heuristic Numerical Methods in Data Processing and in Logic Programming

Abstract: We show that fuzzy logic and other soft computing approaches explain and justify heuristic numerical methods used in data processing and in logic programming, in particular, M-methods in robust statistics, regularization techniques, metric fixed point theorems, etc.

Neuro-fuzzy and soft computing for speaker recognition

Abstract: This paper describes how techniques from the discipline of neuro-fuzzy and soft computing techniques can be used, in conjunction with methodologies from pattern recognition and digital signal processing, to effectively perform speech data classification. In particular, we have applied the proposed method to automatic speaker recognition and achieved satisfactory results.