(特价书)自适应信号处理（英文影印版）

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    通信书籍
本书是自适应信号处理方面的一部经典著作，系统介绍了自适应信号处理的原理、算法和结构及其在各个领域的应用。
本书既有理论分析，又有物理概念阐述，核心内容是自适应线性组合器、LMS算法、自适应模拟与系统辨识、自适应控制系统、自适应波束形成器的分析。本书要求读者具备工程和数学方面的基础知识，并具有编写和运行计算机程序的能力。
本书特色
从特定工程系统的特性或特征引入自适应的概念。
描述一种可用于分析所有自适应系统的几何“性能表面”。
包含性能表面梯度估值的统计分析，以及各种搜索方法的比较。
介绍了LMS算法与线性组合器以及其他自适应算法和结构，如序贯回归算法与自适应格型滤波结构等。
涵盖了自适应信号处理的主要应用。
介绍了自适应模拟与自适应逆模拟及其在通信和控制领域的应用。
包含了自适应干扰对消。
介绍了自适应阵和波束形成。

作者：(美国)斯坦福大学 (美国)新墨西哥大学

LIST OF SYMBOLS
part ⅠGENERAL INTRODUCTION Objectives of Part Ⅰ
　1 ADAPTIVE SYSTEMS
　　Definition. and Characteristics
　　Areas of Application
　　General Properties
　　Open- and Closed-Loop Adaptation
　　Applications of Closed-Loop Adaptation
　　Example of an Adaptive System
　　The Chapters Ahead
　2 THE ADAPTIVE LINEAR COMBINER
　　General Description
　　Input Signal and Weight Vectors
　　Desired Response and Error
　　The Performance Function
　 Gradient and Minimum Mean-Square Error
　　Example of a Performance Surface
　　Alternative Expression of the GradiEnt
　　Decorrelation of Error and Input Components
　　Exercises

　　This book has grown out of nearly three decades of research and teaching in the field of adaptive signal processing. It is designed primarily to be a basic text on adaptive signal processing and, at the time of its publication, it is believed to be the only basic text on the subject, or at least the only textbook covering the breadth of subject matter shown in the table of contents. .
　　The book is based on class notes for a one- ortwo-semester senior or graduate level course in adaptive signal processing taught at Stanford University, the University of New Mexico, and Sandia National Laboratories. Every chapter except Chapter 1 has exercises at the end, and these are considered to be an essential part of any course using the text. The exercises are often used to complete the reader's understanding of a concept or to present different applications of ideas in the text.
　　Referring to the table of contents, the reader can see that the book is divided into four main parts. The first three parts--General Introduction, Theory of Adaptation with Stationary Signals,. and Adaptive Algorithms and Structures--make up a little less than half of the text. The material in these parts is considered basic theory and would normally be included in any first course on adaptive signal processing. The fourth part--Applications--consists of six chapters on various engineering applications of adaptive signal processing. In this part the instructor may wish to concentrate on subjects of special interest. However, even in a one-semester course, the instructor will probably wish to include at least the first portion of each chapter.
　　For prerequisites, we assume that the student has at least senior-level academic experience in engineering and mathematics, and has the ability to write and run computer programs. The latter is essential for doing many of the exercises. A course in linear systems analysis, particularly in discrete systems with the use of the z-transform, would provide a very useful (if not essential) background. Also, a course in engineering statistics or probability, or the equivalent, provides a helpful background.
　　In the first part of the text, Chapter 1 introduces the concept of adaptation as a property or characteristic of certain systems in engineering. Chapter 2 introduces the adaptive linear combiner, which is the simplest and most widely used adaptive structure. Chapter 2 also describes a geometric "performance surface" which is useful in the analysis of all adaptive systems. ..
　　Part II, Theory of Adaptation with Stationary Signals, contains an analysis of the performance surface and its properties. The analysis begins in Chapter 3, and in Chapter 4 adaptation is viewed as the process of searching the performance surface for its minimum. Chapter 5 contains a statistical analysis of gradient estimation on the performance surface and a comparison of search methods.
　　In Part III, Adaptive Algorithms and Structures, the least mean squares (LMS) algorithm is introduced and discussed in Chapter 6, In :Chapter 7 basic signal processing concepts that are required for the rest of the book are introduced. These include primarily the z-transform relationships linking the time and frequency domains. To conclude Part III, Chapter 8 introduces adaptive algorithms other than the LMS algorithm and adaptive structures other than the adaptive linear combiner, including the adaptive lattice structure. The latter is considered, at the time of this writing, to be a rapidly developing area, and our introduction to it is therefore less comprehensive than we would wish it to be.
　　Finally, Part IV covers the major application areas of adaptive signal process- ing. Once the basics in Chapters 1-8 have been learned， subjects can be chosen selectively from Part IV. In Chapters 9 and 10, forward and inverse adaptive modeling are introduced and applied to areas such as multipath communication, geophysical exploration, digital filter design, and telephone channel equalization. Adaptive control systems are introduced in. Chapter 11, and Chapter 12 introduces adaptive interference canceling, with several examples of application. Chapters 13 and' 14 cover adaptive arrays and beamformers:
　　While writing this text, the authors have had the benefit of critiques, com- ments, and suggestions from many talented colleagues. We are very grateful for the reviews and ideas we have received, and thankful for the friendships engendered and increased through this work. We especially wish to acknowledge the help of Robert D. Fraser, Dennis R. Morgan, Dae H. Youn, Eugene Walach, Richard Gooch, Ruth A. David, Sharon K. Fletcher, Claude S. Lindquist, Daksheesh Parikh, Delores M. Etter, Edward S. Angel, Lloyd J. Griffiths, Nasir Ahmed, John R. Treichler, C. Richard Johnson, Jr., Michael G. Larimore, Glenn R. Elliott, John M. McCool, John M. Cioffi, and T. C. Hsia. :The book would not be in its present form without the contributions of these special friends.
　　We also wish to thank all of the students who took the adaptive signal processing courses mentioned above. In effect, they have edited and corrected the text far beyond our ability to do so. We thank all of these students for their patience, interest, and enthusiasm.
　　The only ones with more patience and perseverance than our students have been the talented ladies who have typed and retyped this text, Debra Shepperd at Sandia and Mieko Parker at Stanford. We also acknowledge their help with gratitude. ...
　　Bernard Widrow
　　Samuel D. Steams
　　

　　Bernard Widrow毕业于麻省理工学院，斯坦福大学电子工程系教授。他的主要研究兴趣包括自适应信号处理、自适应控制系统和自适应神经网络。除本书外，他还与他人合著了《Adaptive Inverse Control》一书。