Hagan & Demuth's Neural Network Design 2nd Edition: A Comprehensive Guide

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《神经网络设计(英文第二版)》是由马丁·T·汉根(Martin T. Hagan)、霍华德·B·德穆斯(Howard B. Demuth)、马克·H·比尔(Aaron M. Beale)和奥兰多·德赫苏斯(Orlando De Jesús)合著的一本深度探讨神经网络设计与应用的经典著作。本书是第二版，延续了作者在前一版中的研究成果，旨在为读者提供全面理解神经网络理论和技术的深入指南。在第一章"简介"中，作者首先回顾了神经网络的历史，从早期的生物灵感出发，介绍了这一领域的起源和发展。神经网络的设计最初是受大脑神经元结构和功能的启发，通过模拟人脑的学习和决策过程来解决复杂的问题。这一章节详细阐述了神经网络如何从简单的感知器发展到现代深度学习网络的演变过程。接着，作者概述了神经网络在各个领域的广泛应用，包括图像识别、语音识别、自然语言处理、机器学习、预测分析等，展示了其在人工智能和信息技术领域的核心地位。神经网络因其强大的适应性和泛化能力，在工业控制、医疗诊断、金融风控等领域都有显著的效果。书中还强调了神经网络设计的核心原则，如网络结构（如前馈、反馈、卷积等）、激活函数的选择、训练算法（如反向传播、遗传算法等）以及优化策略。这些内容对于构建高效、可解释的神经网络模型至关重要。为了帮助读者更好地理解和实践，书中提供了丰富的示例和演示程序，这些可以在作者的官方网站hagan.okstate.edu/nnd.html上获取。此外，一个精简的平装版也可以在亚马逊上订购，便于读者在课堂学习或个人研究时参考。在版权方面，作者明确声明未经许可，不得以任何形式复制、存储或录音，体现了对知识产权的尊重。本书的献词部分表达了作者对家人和同事的感谢，展示了他们对知识分享和传承的重视。《神经网络设计(英文第二版)》是一本深入浅出的教材，适合希望深入研究神经网络技术的工程师、研究人员和学生，无论是初学者还是专业人士，都能从中获益匪浅。通过阅读这本书，读者将掌握神经网络设计的基本原理、技术和实战技巧，为其在实际工作和项目中应用神经网络打下坚实的基础。

xiii

Case Study 4: Clustering

Objectives 26-1

Theory and Examples 26-2

Description of the Forest Cover Problem 26-2

Data Collection and Preprocessing 26-4

Selecting the Architecture 26-5

Training the Network 26-6

Validation 26-7

Data Sets 26-11

Epilogue 26-12

Further Reading 27-15

P-1

Preface

This book gives an introduction to basic neural network architectures and

learning rules. Emphasis is placed on the mathematical analysis of these

networks, on methods of training them and on their application to practical

engineering problems in such areas as nonlinear regression, pattern recog-

nition, signal processing, data mining and control systems.

Every effort has been made to present mat

erial in a clear and consistent

manner so that it can be read and applied with ease. We have included

many solved problems to illustrate each topic of discussion. We have also

included a number of case studies in the final chapters to demonstrate

practical issues that arise when using neural networks on real world prob-

lems.

Since this is a book on the design of neural netw

orks, our choice of topics

was guided by two principles. First, we wanted to present the most useful

and practical neural network architectures, learning rules and training

techniques. Second, we wanted the book to be complete in itself and to flow

easily from one chapter to the next. For this reason, various introductory

materials and chapters on applied mathematics are included just before

they are needed for a particular subject. In summary, we have chosen some

topics because of their practical importance in the application of neural

networks, and other topics because of their importance in explaining how

neural networks operate.

We have omitted many topics that might

have been included. We have not,

for instance, made this book a catalog or compendium of all known neural

network architectures and learning rules, but have instead concentrated

on the fundamental concepts. Second, we have not discussed neural net-

work implementation technologies, such as VLSI, optical devices and par-

allel computers. Finally, we do not present the biological and psychological

oundations of neural networks in any depth. These are all important top-

ics, but we hope that we have done the reade

r a service by focusing on those

topics that we consider to be most useful in the design of neural networks

and by treating those topics in some depth.

This book has been organized for a one-seme

ster introductory course in

neural networks at the senior or first-year graduate level. (It is also suit-

able for short courses, self-study and reference.

) The reader is expected to

have some background in linear algebra, probability and differential equa-

tions.

P-3

sic neural network architectures. The notation that is introduced in this

chapter is used throughout the book. In Chapter 3 we present a simple pat-

tern recognition problem and show how it can be solved using three differ-

ent types of neural networks. These three ne

tworks are representative of

the types of networks that are presented in the remainder of the text. In

addition, the pattern recognition problem presented here provides a com-

mon thread of experience throug

hout the book.

Much of the focus of this book will be

on methods for training neural net-

works to perform various tasks. In Ch

apter 4 we introduce learning algo-

rithms and present the first practical algorithm:

the perceptron learning

rule. The perceptron network has fundamental limitations, but it is impor-

tant for historical reasons and is also a useful tool fo

r introducing key con-

cepts that will be applied to more powerful networks in

later chapters.

One of the main objectives of this book

is to explain how neural networks

operate. For this reason we will weave together neural network topics with

important introductory material. For example, linear algebra, which is the

core of the mathematics required for understanding neural networks, is re-

viewed in Chapters 5 and 6. The concept

s discussed in these chapters will

be used extensively throughout the remainder of the book.

Chapters 7, and 15–19 describe networks

and learning rules that are

heavily inspired by biology and psychology. They fall into two categories:

associative networks and competitive networks. Chapters 7 and 15 intro-

duce basic concepts, while Chapters 16

–19 describe more advanced net-

works.

Chapters 8–14 and 17 develop a class of learning called performance learn-

ing, in which a network is trained to o

ptimize its performance. Chapters 8

and 9 introduce the basic concepts of performance learning. Chapters 10–

13 apply these concepts to feedforward neural networks of increasing pow-

er and complexity, Chapter 14 applies them to

dynamic networks and

Chapter 17 applies them to radial basis networks, which also use concepts

from competitive learning.

Chapters 20 and 21 discuss recurrent associative memory network

s. These

networks, which have feedback connections, are dynamical systems. Chap-

ter 20 investigates the stability of the

se systems. Chapter 21 presents the

Hopfield network, which has been one of the most influential recurrent net-

works.

Chapters 22–27 are different than the preceding chapters. Previous chap-

ters focus on the fundamentals of each type of

network and their learning

rules. The focus is on understanding the key concepts. In Chapters 22–27,

we discuss some practical issues in applying neural networks to real world

problems. Chapter 22 describes many practical training tips, and Chapters

23–27 present a series of case studies, in which neural networks are ap-

plied to practical problems in function a

pproximation, probability estima-

tion, pattern recognition, clu

stering and prediction.

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