神经网络设计第二版：Hagan & Demuth著

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"《神经网络设计第2版》是由Martin T. Hagan、Howard B. Demuth、Mark Hudson Beale和Orlando DeJesús合作编写的书籍，版权由作者保留，未经许可不得复制或传播。该书是关于神经网络设计的深度探讨，适用于教育和专业研究。" 在《神经网络设计第2版》中，作者们深入浅出地介绍了神经网络的设计原理和应用。这本书主要涵盖了以下几个方面： 1. 前言：通常会介绍作者编写此书的初衷、目标读者群体以及书中的主要内容和结构。 2. 历史：书中可能会回顾神经网络的发展历程，包括早期的理论基础，如感知器模型，以及后来的多层前馈网络和反向传播算法等重要进展。 3. 应用：作者会列举神经网络在各种领域的应用，比如图像识别、自然语言处理、预测模型、控制系统等，以展示其广泛的实际价值。 4. 生物启发：神经网络的概念源于生物学的神经元模型，书中可能会详细解释生物神经元的工作机制如何启发了人工神经网络的设计。 5. 内容概览：除了上述主题，书中的内容可能还包括神经网络的基本架构、学习算法（如梯度下降、反向传播）、激活函数、优化方法、正则化技术、卷积神经网络、递归神经网络以及深度学习等方面的知识。 6. 附加资源：读者可以通过提供的网站hagan.okstate.edu/nnd.html获取课件、演示程序和其他补充材料，以辅助理解和实践书中介绍的理论。 7. 纸质版本：此外，还提供了一个压缩版的平装本，方便读者购买和携带。 8. 致谢：作者们在书的开头通常会向支持他们的人表示感谢，这包括家人、同事和合作者。这本书对于想要深入理解神经网络设计原理和技术的读者来说，是一份宝贵的资源。它不仅提供了理论知识，还强调了实际应用，使得读者能够将所学应用于解决实际问题。通过学习这本书，读者可以系统地掌握神经网络的设计与实现，为在人工智能和机器学习领域进行研究或工作打下坚实的基础。

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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