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Neural Networks and Fuzzy-logic Control on Personal Computers and Workstations

Neural Networks and Fuzzy-logic Control on Personal Computers and Workstations
Author: Granino Arthur Korn
Publisher: MIT Press (MA)
Total Pages: 418
Release: 1995
Genre: Computers
ISBN:

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Neural Networks and Fuzzy-Logic Control introduces a simple integrated environment for programming displays and report generation. It includes the only currently available software that permits combined simulation of multiple neural networks, fuzzy-logic controllers, and dynamic systems such as robots or physiological models. The enclosed educational version of DESIRE/NEUNET differs for the full system mainly in the size of its data area and includes a compiler, two screen editors, color graphics, and many ready-to-run examples. The software lets users or instructors add their own help screens and interactive menus. The version of DESIRE/NEUNET included here is for PCs, viz. 286/287, 386/387, 486DX, Pentium, P6, SX with math coprocessor.


Modelling, Simulation and Control of Non-linear Dynamical Systems

Modelling, Simulation and Control of Non-linear Dynamical Systems
Author: Patricia Melin
Publisher: CRC Press
Total Pages: 149
Release: 2001-10-25
Genre: Mathematics
ISBN: 1000611965

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These authors use soft computing techniques and fractal theory in this new approach to mathematical modeling, simulation and control of complexion-linear dynamical systems. First, a new fuzzy-fractal approach to automated mathematical modeling of non-linear dynamical systems is presented. It is illustrated with examples on the PROLOG programming la


Soft Computing for Control of Non-Linear Dynamical Systems

Soft Computing for Control of Non-Linear Dynamical Systems
Author: Oscar Castillo
Publisher: Physica
Total Pages: 231
Release: 2012-12-06
Genre: Computers
ISBN: 3790818321

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This book presents a unified view of modelling, simulation, and control of non linear dynamical systems using soft computing techniques and fractal theory. Our particular point of view is that modelling, simulation, and control are problems that cannot be considered apart, because they are intrinsically related in real world applications. Control of non-linear dynamical systems cannot be achieved if we don't have the appropriate model for the system. On the other hand, we know that complex non-linear dynamical systems can exhibit a wide range of dynamic behaviors ( ranging from simple periodic orbits to chaotic strange attractors), so the problem of simulation and behavior identification is a very important one. Also, we want to automate each of these tasks because in this way it is more easy to solve a particular problem. A real world problem may require that we use modelling, simulation, and control, to achieve the desired level of performance needed for the particular application.


Fuzzy Sets, Neural Networks, and Soft Computing

Fuzzy Sets, Neural Networks, and Soft Computing
Author: Ronald R. Yager
Publisher: Van Nostrand Reinhold Company
Total Pages: 456
Release: 1994
Genre: Computers
ISBN:

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Brings together chapters by experts involved in a new area based on the confluence of genetic algorithms, fuzzy systems, and neural networks. Papers cover the broad ground of fuzzy logic control, neural fuzzy systems, genetic fuzzy systems, process control, and adaptive systems. Topics include the composition of heterogeneous control laws, ellipsoidal learning and fuzzy throttle control for platoons of smart cars, supervised and unsupervised learning, and propagation and satisfaction of flexible constraints. Annotation copyright by Book News, Inc., Portland, OR


Neural Fuzzy Control Systems with Structure and Parameter Learning

Neural Fuzzy Control Systems with Structure and Parameter Learning
Author: Chin-Teng Lin
Publisher: World Scientific Publishing Company
Total Pages: 144
Release: 1994-02-08
Genre:
ISBN: 9813104708

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A general neural-network-based connectionist model, called Fuzzy Neural Network (FNN), is proposed in this book for the realization of a fuzzy logic control and decision system. The FNN is a feedforward multi-layered network which integrates the basic elements and functions of a traditional fuzzy logic controller into a connectionist structure which has distributed learning abilities. In order to set up this proposed FNN, the author recommends two complementary structure/parameter learning algorithms: a two-phase hybrid learning algorithm and an on-line supervised structure/parameter learning algorithm. Both of these learning algorithms require exact supervised training data for learning. In some real-time applications, exact training data may be expensive or even impossible to get. To solve this reinforcement learning problem for real-world applications, a Reinforcement Fuzzy Neural Network (RFNN) is further proposed. Computer simulation examples are presented to illustrate the performance and applicability of the proposed FNN, RFNN and their associated learning algorithms for various applications.


Solving Problems in Environmental Engineering and Geosciences with Artificial Neural Networks

Solving Problems in Environmental Engineering and Geosciences with Artificial Neural Networks
Author: Farid U. Dowla
Publisher: MIT Press
Total Pages: 258
Release: 1995
Genre: Computers
ISBN: 9780262041485

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This book, complete with exercises and ANN algorithms, illustrates how ANNs can be used in solving problems in environmental engineering and the geosciences, and provides the necessary tools to get started using these elegant and efficient new techniques.


Intelligent Control

Intelligent Control
Author: Christopher John Harris
Publisher: World Scientific
Total Pages: 412
Release: 1993
Genre: Computers
ISBN: 9789810210427

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With increasing demands for high precision autonomous control over wide operating envelopes, conventional control engineering approaches are unable to adequately deal with system complexity, nonlinearities, spatial and temporal parameter variations, and with uncertainty. Intelligent Control or self-organising/learning control is a new emerging discipline that is designed to deal with problems. Rather than being model based, it is experiential based. Intelligent Control is the amalgam of the disciplines of Artificial Intelligence, Systems Theory and Operations Research. It uses most recent experiences or evidence to improve its performance through a variety of learning schemas, that for practical implementation must demonstrate rapid learning convergence, be temporally stable, be robust to parameter changes and internal and external disturbances. It is shown in this book that a wide class of fuzzy logic and neural net based learning algorithms satisfy these conditions. It is demonstrated that this class of intelligent controllers is based upon a fixed nonlinear mapping of the input (sensor) vector, followed by an output layer linear mapping with coefficients that are updated by various first order learning laws. Under these conditions self-organising fuzzy logic controllers and neural net controllers have common learning attributes.A theme example of the navigation and control of an autonomous guided vehicle is included throughout, together with a series of bench examples to demonstrate this new theory and its applicability.


Modeling and Simulation: Theory and Practice

Modeling and Simulation: Theory and Practice
Author: George A. Bekey
Publisher: Springer Science & Business Media
Total Pages: 293
Release: 2012-12-06
Genre: Science
ISBN: 1461502357

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Modeling and Simulation: Theory and Practice provides a comprehensive review of both methodologies and applications of simulation and modeling. The methodology section includes such topics as the philosophy of simulation, inverse problems in simulation, simulation model compilers, treatment of ill-defined systems, and a survey of simulation languages. The application section covers a wide range of topics, including applications to environmental management, biology and medicine, neural networks, collaborative visualization and intelligent interfaces. The book consists of 13 invited chapters written by former colleagues and students of Professor Karplus. Also included are several short 'reminiscences' describing Professor Karplus' impact on the professional careers of former colleagues and students who worked closely with him over the years.


Multiple Approaches to Intelligent Systems

Multiple Approaches to Intelligent Systems
Author: Ibrahim F. Imam
Publisher: Springer
Total Pages: 918
Release: 2004-05-19
Genre: Computers
ISBN: 3540487654

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We never create anything, We discover and reproduce. The Twelfth International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems has a distinguished theme. It is concerned with bridging the gap between the academic and the industrial worlds of Artificial Intelligence (AI) and Expert Systems. The academic world is mainly concerned with discovering new algorithms, approaches, and methodologies; however, the industrial world is mainly driven by profits, and concerned with producing new products or solving customers’ problems. Ten years ago, the artificial intelligence research gap between academia and industry was very broad. Recently, this gap has been narrowed by the emergence of new fields and new joint research strategies in academia. Among the new fields which contributed to the academic-industrial convergence are knowledge representation, machine learning, searching, reasoning, distributed AI, neural networks, data mining, intelligent agents, robotics, pattern recognition, vision, applications of expert systems, and others. It is worth noting that the end results of research in these fields are usually products rather than empirical analyses and theoretical proofs. Applications of such technologies have found great success in many domains including fraud detection, internet service, banking, credit risk and assessment, telecommunication, etc. Progress in these areas has encouraged the leading corporations to institute research funding programs for academic institutes. Others have their own research laboratories, some of which produce state of the art research.


Fuzzy Logic and Neural Network Handbook

Fuzzy Logic and Neural Network Handbook
Author: Chi-hau Chen
Publisher: McGraw-Hill Companies
Total Pages: 862
Release: 1996
Genre: Computers
ISBN:

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A practical reference that presents concise and comprehensive reports on the major activities in fuzzy logic and neural networks, with emphasis on the applications and systems of interest to computer engineers. Each of the 31 chapters focuses on the most important activity of a specific topic, and the chapters are organized into three parts: principles and algorithms; applications; and architectures and systems. The applications for fuzzy logic include home appliance design and manufacturing process; those for neural networks include radar, sonar, and speech signal processing, remote sensing, and electrical power systems. Annotation copyright by Book News, Inc., Portland, OR