Artificial Neural Systems: Principles and Practice


by

Pierre Lorrentz

DOI: 10.2174/97816810809011150101
eISBN: 978-1-68108-090-1, 2015
ISBN: 978-1-68108-091-8



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An intelligent system is one which exhibits characteristics including, but not limited to, learning, adaptation, and problem-solving. ...[view complete introduction]

Table of Contents

Foreword

- Pp. i-ii (2)

Rajkumar Roy

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Preface

- Pp. iii-iv (2)

Pierre Lorrentz

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Neurons

- Pp. 4-14 (11)

Pierre Lorrentz

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Basic Neurons

- Pp. 15-27 (13)

Pierre Lorrentz

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Basic Fuzzy Neuron and Fundamentals of ANN

- Pp. 28-39 (12)

Pierre Lorrentz

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Fundamental Algorithms and Methods

- Pp. 40-68 (29)

Pierre Lorrentz

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Quantum Logic and Classical Connectivity

- Pp. 69-86 (18)

Pierre Lorrentz

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Learning Methods

- Pp. 88-116 (29)

Pierre Lorrentz

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Neural Networks

- Pp. 118-153 (36)

Pierre Lorrentz

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Selection and Combination Strategy of ANN Systems

- Pp. 154-170 (17)

Pierre Lorrentz

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Probability-based Neural Network Systems

- Pp. 171-197 (27)

Pierre Lorrentz

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Emerging Networks

- Pp. 198-216 (19)

Pierre Lorrentz

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Research and Developments in Neural Networks

- Pp. 217-235 (19)

Pierre Lorrentz

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Subject Index

- Pp. 235

Pierre Lorrentz

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Foreword

Neural Networks, Fuzzy Logic and Evolutionary Computing are members of Soft Computing class of techniques. The techniques are capable of identifying and handling inexact solutions for complex tasks and can deal with the real life uncertainties within the computational framework. Soft Computing has significantly matured over the years and we can find significant applications of soft computing in industry and research environment. Neural Networks is the most mature of other techniques in the Soft Computing. The networks have also benefitted from integrated Fuzzy Logic based systems to model complex engineering systems with the human expert knowledge and through robust system modelling.

Evolutionary Computing helps to optimise the design a Neural Network. Each members of the Soft Computing has several algorithms and concepts that need better understanding for application development.

This book on ‘artificial neural networks – principle and practice’ provides necessary foundation to understand the basics of Neural Networks and how to develop real life applications. From basic definitions to relevant theorems the book presents an algorithm approach to describe the foundations.

The book also emphasises systematic approach to Intelligent System analysis and design. In order to build a Neural Network or Artificial Neural Network application, one needs to apply knowledge of probability based methods as well as fuzzy sets for more uncertain aspects of the problem. The book then explains the motivation from our neural system to develop the Neural Networks.

Description of other network-based approaches using nodes and edges also strengthens the understanding about the Neural Networks. A major strength of the book is the fundamentals of quantum logic for emerging Neural Network development. This is major area for future development. A discussion on Neural Network hardware would have strengthened the book.

The second part of the book presents detailed discussion on learning algorithms, current and emerging Neural Network structures and application development. The chapters also present metrics to evaluate effectiveness of the network. Selection and integration of multiple Neural Networks to solve a real life and complex problem is a major aspect of the book. As mentioned before there are several algorithms and approaches to solve a problem, the systematic approach presented in the book is of major interest. Application of the network to solve a complex modelling task usually requires significant volume of data. A further discussion on the modelling approaches with less data would be very helpful.

The emphasis on probability based neural network and its application is significant because of its popularity. But the real strength of this part of the book is in describing the Quantum Neural Networks and the Deep Belief Network (DBN).

Finally the book also outlines the research and development in Neural Networks. Future Neural Networks are learning from specialised parts of our neural system and trying to scale up to solve even more complex engineering applications.

Rajkumar Roy
Cranfield University, UK


Preface

An intelligent system is that which exhibit characteristics of learning, adaptation, and problem-solving, among others. The group of intelligent system, conceived and designed by human, is loosely termed Artificial Neural Network (ANN) System. Such ANN system is the theme of the book. The book also describe nets (also called network or graphs), evolutionary methods, clustering algorithm, and others nets, most of which are complementary to ANN system.

The term “practice” in the title refers to design, analysis, performances assessment, and testing. The design and analysis may be facilitated by the explanations, equations, diagrams, and algorithms given. Performance assessments occur in any section that bear the name and apply to any ANN system because they are standard independent methods and most ANN system has an associated error feedback. Testing is exemplified by case studies and is given toward the end of most chapters.

An interest in artificial neural sciences is a sufficient requirement to understand the content of the book, though knowledge of signal processing, mathematics, and electrical/electronic communication is an advantage. The book specifically takes a developmental perspective, making it more beneficial for professionals. The book adopts a spiral method of description whereby various topics are revisited several times; each visit introduces fresh material at increasing level of sophistication. Each visit to a specific ANN type may also introduce new ANN system(s) and/or new algorithm(s) as the case may be.

The book is divided into two parts (I and II). Part I contain five chapters. Chapter 1 introduce the biological neurons and basic artificial neurons. From these, chapter 2 derive better neurons and introduce statistical methods. Chapter 3 describe a framework of dynamic fuzzyneuron, and explain the fundamental principle governing the design and analysis of ANN system. To distinguish other algorithms (e.g. clustering algorithm) from learning algorithms, chapter 4 describe fundamentals of genetic algorithm, clustering algorithms, and those other algorithms complementary to ANN systems. Neural network is in chapter 3 introduced by graph. Chapter 5 concludes part 1 by introducing quantum neural network, quantum maths and logic. The chapter also describe Hodgkin-Huxley neuron, and memristance.

Similarly, part II consists of six chapters. In Chapter 6, artificial neuromorphic network, and Widrow-Hoff learning are visited; so is fuzzy ANN system. While chapter 7 describes the usual weighted, weightless ANN systems. It also introduces Bayesian ANNs, and discusses general performance assessment methods. On the other hand, chapter 8 considers various selection and combination strategy for ANN systems. Chapter 9 is dedicated to Bayesian networks. There are some promising ANN systems being considered in the research arena, and also now in chapter 10, these ANN may revolutionize ANN throughput in future. In chapter 11 implementation issues regarding Monte Carlo algorithm is visited, and also implementation issues regarding neuromorphic networks is revisited.

The book attempts to impart considerable knowledge of know-how of ANN to the reader in order to facilitate a novel development and research. Albeit also improve an ad-hoc ANN. This may encourage and help a developer to meet any industrial increasing demand for novel ANNs’ implementation and application.

Pierre Lorrentz
University of Kent,
United Kingdom

List of Contributors

Editor(s):
Pierre Lorrentz
University of Kent
United Kingdom




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