Computational Intelligence: An Introduction, 2/e (Hardcover)

Andries P. Engelbrecht

  • 出版商: Wiley
  • 出版日期: 2007-11-01
  • 定價: $1,600
  • 售價: 9.5$1,520
  • 語言: 英文
  • 頁數: 628
  • 裝訂: Hardcover
  • ISBN: 0470035617
  • ISBN-13: 9780470035610
  • 相關分類: 人工智慧
  • 立即出貨 (庫存=1)

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商品描述

Description 

The main focus of the book is on computational modelling of biological and natural intelligent systems in order to develop nature inspired artificially intelligent systems. These algorithmic models have as their main objective to facilitate the implementation of artificial intelligent systems for solving complex real-world systems (e.g. fuzzy systems are applied successfully to control systems, gear transmission, breaking systems; swarm intelligence to image classification).

This second edition expands on all these paradigms, providing a more detailed and equal treatment of them all. Most recent advances in CI have been added, namely artificial immune systems, hybrid systems, and a section on how to perform empirical studies.

 

Table of Contents

 

Page

List of Tables

List of Figures

List of Algorithms

Preface

Part I INTRODUCTION

1 Introduction to Computational Intelligence

Part II ARTIFICIAL NEURAL NETWORKS

2 The Artificial Neuron 

3 Supervised Learning Neural Networks

4 Unsupervised Learning Neural Networks

5 Radial Basis Function Networks 

6 Reinforcement Learning

7 Performance Issues (Supervised Learning)

Part III EVOLUTIONARY COMPUTATION

8 Introduction to Evolutionary Computation

9 Genetic Algorithms

10 Genetic Programming

11 Evolutionary Programming

12 Evolution Strategies

13 Differential Evolution

14 Cultural Algorithms

15 Coevolution

Part IV COMPUTATIONAL SWARM INTELLIGENCE

16 Particle Swarm Optimization

17 Ant Algorithms

Part V ARTIFICIAL IMMUNE SYSTEMS

18 Natural Immune System

19 Artificial Immune Models

Part VI FUZZY SYSTEMS

20 Fuzzy Sets

21 Fuzzy Logic and Reasoning

22 Fuzzy Controllers

23 Rough Sets

24 FINAL REMARKS

References

A Optimization Theory

 

商品描述(中文翻譯)

描述

本書的主要焦點是計算模擬生物和自然智能系統,以開發受自然啟發的人工智能系統。這些算法模型的主要目標是為了實現解決複雜現實世界系統的人工智能系統(例如,模糊系統成功應用於控制系統、齒輪傳動、制動系統;群體智能應用於圖像分類)。

第二版擴展了所有這些範例,更詳細且平等地對待它們。最新的計算智能進展已經添加,包括人工免疫系統、混合系統以及如何進行實證研究的部分。

目錄

頁面
表格清單
圖片清單
算法清單
前言
第一部分:介紹
1. 計算智能簡介
第二部分:人工神經網絡
2. 人工神經元
3. 監督學習神經網絡
4. 非監督學習神經網絡
5. 徑向基函數網絡
6. 強化學習
7. 性能問題(監督學習)
第三部分:進化計算
8. 進化計算簡介
9. 遺傳算法
10. 遺傳編程
11. 進化編程
12. 進化策略
13. 差分進化
14. 文化算法
15. 共進化
第四部分:計算群體智能
16. 粒子群優化