Complex-Valued Neural Networks / Edition 1

Complex-Valued Neural Networks / Edition 1

by Akira Hirose
     
 

ISBN-10: 3540334564

ISBN-13: 9783540334569

Pub. Date: 10/05/2006

Publisher: Springer-Verlag New York, LLC

This book is the second enlarged and revised edition of the first successful monograph on complex-valued neural networks (CVNNs) published in 2006, which lends itself to graduate and undergraduate courses in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering, and other relevant fields.

In the second edition the recent

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Overview

This book is the second enlarged and revised edition of the first successful monograph on complex-valued neural networks (CVNNs) published in 2006, which lends itself to graduate and undergraduate courses in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering, and other relevant fields.

In the second edition the recent trends in CVNNs research are included, resulting in e.g. almost a doubled number of references. The parametron invented in 1954 is also referred to with discussion on analogy and disparity. Also various additional arguments on the advantages of the complex-valued neural networks enhancing the difference to real-valued neural networks are given in various sections.

The book is useful for those beginning their studies, for instance, in adaptive signal processing for highly functional sensing and imaging, control in unknown and changing environment, robotics inspired by human neural systems, and brain-like information processing, as well as interdisciplinary studies to realize comfortable society. It is also helpful to those who carry out research and development regarding new products and services at companies. The author wrote this book hoping in particular that it provides the readers with meaningful hints to make good use of neural networks in fully practical applications. The book emphasizes basic ideas and ways of thinking. Why do we need to consider neural networks that deal with complex numbers? What advantages do the complex-valued neural networks have? What is the origin of the advantages? In what areas do they develop principal applications? This book answers these questions by describing details and examples, which will inspire the readers with new ideas.

The book is useful for those beginning their studies, for instance, in adaptive signal processing for highly functional sensing and imaging, control in unknown and changing environment, robotics inspired by human neural systems, and brain-like information processing, as well as interdisciplinary studies to realize comfortable society. It is also helpful to those who carry out research and development regarding new products and services at companies. The author wrote this book hoping in particular that it provides the readers with meaningful hints to make good use of neural networks in fully practical applications. The book emphasizes basic ideas and ways of thinking. Why do we need to consider neural networks that deal with complex numbers? What advantages do the complex-valued neural networks have? What is the origin of the advantages? In what areas do they develop principal applications? This book answers these questions by describing details and examples, which will inspire the readers with new ideas.

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Product Details

ISBN-13:
9783540334569
Publisher:
Springer-Verlag New York, LLC
Publication date:
10/05/2006
Series:
Studies in Computational Intelligence Series, #32
Pages:
192
Product dimensions:
6.30(w) x 9.50(h) x 0.70(d)

Related Subjects

Table of Contents

Complex-valued neural networks fertilize electronics.- Neural networks: The characteristic viewpoints.- Complex-valued neural networks: Distinctive features.- Constructions and dynamics of neural networks.- Land-surface classification with unevenness and reflectance taken into consideration.- Adaptive radar system to visualize antipersonnel plastic landmines.- Removal of phase singular points to create digital elevation map.- Lightwave associative memory that memorizes and recalls information depending on optical-carrier frequency.- Adaptive optical-phase equalizer.- Developmental learning with behavioral-mode tuning by carrier-frequency modulation.- Pitch-asynchronous overlap-add waveform-concatenation speech synthesis by optimizing phase spectrum in frequency domain.

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