| 000 | 00693camuuu200217 a 4500 | |
| 001 | 000000900154 | |
| 005 | 19990107155346.0 | |
| 008 | 920615s1994 maua b 001 0 eng d | |
| 010 | ▼a 92002345 //r942 | |
| 020 | ▼a 020156629X | |
| 040 | ▼a 244002 ▼c 244002 | |
| 049 | 0 | ▼l 151003450 |
| 082 | 0 4 | ▼a 006.3 ▼2 20 |
| 090 | ▼a 006.3 ▼b F855s | |
| 100 | 1 | ▼a Freeman, James A. |
| 245 | 1 0 | ▼a Simulating neural networks with Mathematica / ▼c James A. Freeman. |
| 260 | ▼a Reading, Mass. : ▼b Addison-Wesley, ▼c c1994. | |
| 300 | ▼a x, 341 p. : ▼b ill. ; ▼c 25 cm. | |
| 504 | ▼a Includes bibliographical references (p. 335-336) and index. | |
| 630 | 0 0 | ▼a Mathematica (Computer file) |
| 650 | 0 | ▼a Neural networks (Computer science). |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 세종학술정보원/과학기술실(5층)/ | 청구기호 006.3 F855s | 등록번호 151003450 | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
This book introduces neural networks, their operation, and application, in the context of the interactive Mathematica environment. Readers will learn how to simulate neural network operations using Mathematica, and will learn techniques for employing Mathematica to assess neural network behavior and performance. For students of neural networks in upper-level undergraduate or beginning graduate courses in computer science, engineering, and related areas. Also for researchers and practitioners interested in using Mathematica as a research tool.
Features- Teaches the reader about what neural networks are, and how to manipulate them within the Mathematica environment.
- Shows how Mathematica can be used to implement and experiment with neural network architectures.
- Addresses a major topic related to neural networks in each chapter, or a specific type of neural network architecture.
- Contains exercises, suggested projects, and supplementary reading lists with each chapter.
- Includes Mathematica application programs (packages) in Appendix. (Also available electronically from MathSource.)
Training by Error Minimization
Backpropagation and Its Variants
Probability and Neural Networks
Optimization and Constraint Satisfaction with Neural Networks
Feedback and Recurrent Networks
Adaptive Resonance Theory
Genetic Algorithms
020156629XB04062001
정보제공 :
목차
CONTENTS Preface = iii 1 Introduction to Neural Networks and Mathematica = 1 1.1 The Neural-Network Paradigm = 2 1.2 Neural-Network Fundamentals = 7 2 Training by Error Minimization = 39 2.1 Adaline and the Adaptive Linear Combiner = 40 2.2 The LMS Learning Rule = 42 2.3 Error Minimization in Multilayer Networks = 63 3 Backpropagation and Its Veriants = 67 3.1 The Generalized Delta Rule = 68 3.2 BPN Examples = 74 3.3 BPN Variations = 97 3.4 The Functional Link Network = 103 4 Probability and Neural Networks = 115 4.1 The Discrete Hopfield Network = 116 4.2 Stochastic Methods for Neural Networks = 124 4.3 Bayesian Pattern Classification = 135 4.4 The Probabilistic Neural Network = 144 5 Optimization and Constraint Satisfaction = 153 5.1 The Traveling Salesperson Problem(TSP) = 154 5.2 Neural Networks and the TSP = 156 6 Feedback and Recurrent Networks = 177 6.1 The BAM = 178 6.2 Recognition of Time Sequences = 185 7 Adaptive Resonance Theory = 209 7.1 ART1 = 211 7.2 ART2 = 243 8 Genetic Algorithms = 259 8.1 GA Basics = 260 8.2 A Basic Genetic Algorithm(BGA) = 266 8.3 A GA for Training Neural Networks = 281 Appendix A Code Listings = 295 Bibliography = 335 Index = 337