Hagan & Demuth's Neural Network Design: A Foundation for Deep Learning

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《神经网络设计》第二版是由马丁·T·哈根(Martin T. Hagan)、霍华德·B·德穆斯(Howard B. Demuth)、马克·H·比尔(Mark H. Beale)和奥兰多·德赫苏斯(Orlando De Jesús)共同编著的一本深度学习入门级的经典教材。这本书专注于神经网络的基础知识，适合那些希望深入了解这一领域的初学者和专业人士。作者们结合各自的专业背景，包括霍华德·德穆斯来自科罗拉多大学博尔德分校，而马克·比尔则代表了MHB Inc.，奥兰多·德赫苏斯则以顾问的身份参与，共同为读者提供了一个全面且深入的学习平台。该书在第1章中，首先概述了神经网络的历史，介绍了这一概念的发展脉络，从其起源于生物神经元的工作原理到早期计算机科学中的应用。作者们解释了神经网络如何模拟人脑的信息处理方式，以及它们在诸如图像识别、自然语言处理和预测分析等领域的潜在应用。此外，他们还讨论了神经网络设计的生物灵感，强调了它们在理解和模仿生物大脑结构与功能方面的价值。接着，章节内容深入到神经网络的具体设计方法和技术，如感知器、多层感知机、反向传播算法等。书中详细阐述了这些技术背后的数学原理，以及如何通过调整权重和激活函数来优化网络性能。同时，作者也提到了神经网络的局限性以及未来可能的研究方向。值得注意的是，本书还包含了配套的示例程序和教学资源，可以在作者的官方网站<http://hagan.okstate.edu/nnd.html>上找到。对于纸质书版本，读者可以通过亚马逊等渠道获得一个浓缩版的简装版。版权信息明确指出，未经哈根和德穆斯的许可，任何形式的复制、存储或转录都是不被允许的。《神经网络设计》第二版是一本实用且系统化的教材，无论是对初次接触神经网络的学生还是已经在该领域有一定经验的工程师，都能从中受益匪浅。它不仅提供了理论知识，还为实践操作和进一步研究提供了坚实的基础。通过阅读这本书，读者将能掌握神经网络设计的基本概念、技术及其实现细节，为进一步探索深度学习和其他人工智能技术打下坚实的基础。

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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