Soft Computing and Intelligent Systems Design

Soft Computing and Intelligent Systems Design PDF

Author: Fakhreddine O. Karray

Publisher: Addison Wesley Longman

Published: 2004

Total Pages: 592

ISBN-13:

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Traditional artificial intelligence (AI) techniques are based around mathematical techniques of symbolic logic, with programming in languages such as Prolog and LISP invented in the 1960s. These are referred to as "crisp" techniques by the soft computing community. The new wave of AI methods seeks inspiration from the world of biology, and is being used to create numerous real-world intelligent systems with the aid of soft computing tools. These new methods are being increasingly taught at the upper end of the curriculum, sometimes as an adjunct to traditional AI courses, and sometimes as a replacement for them. Where a more radical approach is taken and the course is being taught at an introductory level, we have recently published Negnevitsky's book. Karray and Silva will be suitable for the majority of courses which will be found at an advanced level. Karray and de Silva cover the problem of control and intelligent systems design using soft-computing techniques in an integrated manner. They present both theory and applications, including industrial applications, and the book contains numerous worked examples, problems and case studies. Covering the state-of-the-art in soft-computing techniques, the book gives the reader sufficient knowledge to tackle a wide range of complex systems for which traditional techniques are inadequate.

New Soft Computing Techniques for System Modeling, Pattern Classification and Image Processing

New Soft Computing Techniques for System Modeling, Pattern Classification and Image Processing PDF

Author: Leszek Rutkowski

Publisher: Springer

Published: 2013-03-09

Total Pages: 376

ISBN-13: 354040046X

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Science has made great progress in the twentieth century, with the establishment of proper disciplines in the fields of physics, computer science, molecular biology, and many others. At the same time, there have also emerged many engineering ideas that are interdisciplinary in nature, beyond the realm of such orthodox disciplines. These in clude, for example, artificial intelligence, fuzzy logic, artificial neural networks, evolutional computation, data mining, and so on. In or der to generate new technology that is truly human-friendly in the twenty-first century, integration of various methods beyond specific disciplines is required. Soft computing is a key concept for the creation of such human friendly technology in our modern information society. Professor Rutkowski is a pioneer in this field, having devoted himself for many years to publishing a large variety of original work. The present vol ume, based mostly on his own work, is a milestone in the devel opment of soft computing, integrating various disciplines from the fields of information science and engineering. The book consists of three parts, the first of which is devoted to probabilistic neural net works. Neural excitation is stochastic, so it is natural to investi gate the Bayesian properties of connectionist structures developed by Professor Rutkowski. This new approach has proven to be par ticularly useful for handling regression and classification problems vi Preface in time-varying environments. Throughout this book, major themes are selected from theoretical subjects that are tightly connected with challenging applications.

Soft Computing In Systems And Control Technology

Soft Computing In Systems And Control Technology PDF

Author: Spyros Tzafestas

Publisher: World Scientific

Published: 1999-05-21

Total Pages: 510

ISBN-13: 981449643X

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Soft computing is a branch of computing which, unlike hard computing, can deal with uncertain, imprecise and inexact data. The three constituents of soft computing are fuzzy-logic-based computing, neurocomputing, and genetic algorithms. Fuzzy logic contributes the capability of approximate reasoning, neurocomputing offers function approximation and learning capabilities, and genetic algorithms provide a methodology for systematic random search and optimization. These three capabilities are combined in a complementary and synergetic fashion.This book presents a cohesive set of contributions dealing with important issues and applications of soft computing in systems and control technology. The contributions include state-of-the-art material, mathematical developments, fresh results, and how-to-do issues. Among the problems studied via neural, fuzzy, neurofuzzy and genetic methodologies are: data fusion, reinforcement learning, approximation properties, multichannel imaging, signal processing, system optimization, gaming, and several forms of control.The book can serve as a reference for researchers and practitioners in the field. Readers can find in it a large amount of useful and timely information, and thus save considerable effort in searching for other scattered literature.

Soft Computing Systems

Soft Computing Systems PDF

Author: Ajith Abraham

Publisher: John Wiley & Sons

Published: 2002

Total Pages: 1244

ISBN-13: 9781586032975

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Annotation Intelligent Systems cover a broad area of knowledge-based systems, computational intelligence, soft computing, and their hybrid combinations. Research and development in intelligent systems have enabled us to not only solve a range of problems which were previously considered too difficult but also have enabled a larger number of other problems to be tacked more effectively. This volume focuses on the recent research developments on intelligent systems in a hybrid environment and its applications in image processing, internet modelling and data mining. The different contributions presented in this volume were accepted for the Second International Conference on Hybrid Intelligent Systems (HIS'02).

Learning and Soft Computing

Learning and Soft Computing PDF

Author: Vojislav Kecman

Publisher: MIT Press

Published: 2001

Total Pages: 556

ISBN-13: 9780262112550

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This textbook provides a thorough introduction to the field of learning from experimental data and soft computing. Support vector machines (SVM) and neural networks (NN) are the mathematical structures, or models, that underlie learning, while fuzzy logic systems (FLS) enable us to embed structured human knowledge into workable algorithms. The book assumes that it is not only useful, but necessary, to treat SVM, NN, and FLS as parts of a connected whole. Throughout, the theory and algorithms are illustrated by practical examples, as well as by problem sets and simulated experiments. This approach enables the reader to develop SVM, NN, and FLS in addition to understanding them. The book also presents three case studies: on NN-based control, financial time series analysis, and computer graphics. A solutions manual and all of the MATLAB programs needed for the simulated experiments are available.

Soft Computing Systems

Soft Computing Systems PDF

Author: Ivan Zelinka

Publisher:

Published: 2018

Total Pages: 871

ISBN-13: 9789811319372

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This book (CCIS 837) constitutes the refereed proceedings of the Second International Conference on Soft Computing Systems, ICSCS 2018, held in Sasthamcotta, India, in April 2018. The 87 full papers were carefully reviewed and selected from 439 submissions. The papers are organized in topical sections on soft computing, evolutionary algorithms, image processing, deep learning, artificial intelligence, big data analytics, data minimg, machine learning, VLSI, cloud computing, network communication, power electronics, green energy.

Soft Computing

Soft Computing PDF

Author: Andrea Tettamanzi

Publisher: Springer Science & Business Media

Published: 2013-04-17

Total Pages: 335

ISBN-13: 3662043351

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Soft computing encompasses various computational methodologies, which, unlike conventional algorithms, are tolerant of imprecision, uncertainty, and partial truth. Soft computing technologies offer adaptability as a characteristic feature and thus permit the tracking of a problem through a changing environment. Besides some recent developments in areas like rough sets and probabilistic networks, fuzzy logic, evolutionary algorithms, and artificial neural networks are core ingredients of soft computing, which are all bio-inspired and can easily be combined synergetically. This book presents a well-balanced integration of fuzzy logic, evolutionary computing, and neural information processing. The three constituents are introduced to the reader systematically and brought together in differentiated combinations step by step. The text was developed from courses given by the authors and offers numerous illustrations as

Fuzzy Logic And Soft Computing

Fuzzy Logic And Soft Computing PDF

Author: Bernadette Bouchon-meunier

Publisher: World Scientific

Published: 1995-09-15

Total Pages: 509

ISBN-13: 9814500089

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Soft computing is a new, emerging discipline rooted in a group of technologies that aim to exploit the tolerance for imprecision and uncertainty in achieving solutions to complex problems. The principal components of soft computing are fuzzy logic, neurocomputing, genetic algorithms and probabilistic reasoning.This volume is a collection of up-to-date articles giving a snapshot of the current state of the field. It covers the whole expanse, from theoretical foundations to applications. The contributors are among the world leaders in the field.

Applications of Image Processing and Soft Computing Systems in Agriculture

Applications of Image Processing and Soft Computing Systems in Agriculture PDF

Author: Razmjooy, Navid

Publisher: IGI Global

Published: 2019-02-22

Total Pages: 337

ISBN-13: 152258028X

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The variety and abundance of qualitative characteristics of agricultural products have been the main reasons for the development of different types of non-destructive methods (NDTs). Quality control of these products is one of the most important tasks in manufacturing processes. The use of control and automation has become more widespread, and new approaches provide opportunities for production competition through new technologies. Applications of Image Processing and Soft Computing Systems in Agriculture examines applications of artificial intelligence in agriculture and the main uses of shape analysis on agricultural products such as relationships between form and genetics, adaptation, product characteristics, and product sorting. Additionally, it provides insights developed through computer vision techniques. Highlighting such topics as deep learning, agribusiness, and augmented reality, it is designed for academicians, researchers, agricultural practitioners, and industry professionals.

Modelling, Simulation and Control of Non-linear Dynamical Systems

Modelling, Simulation and Control of Non-linear Dynamical Systems PDF

Author: Patricia Melin

Publisher: CRC Press

Published: 2001-10-25

Total Pages: 262

ISBN-13: 1420024523

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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