书籍详情
人工智能：智能系统指南（英文版）
作者：（澳）Michael Negnevitsky著
出版社：机械工业出版社
出版时间：2005-01-01
ISBN：9787111158363
定价：¥39.00
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内容简介
人工智能经常被人们认为是计算机科学中的一门高度复杂甚至令人生畏的学科。长期以来人工智能方面的书籍往往包含复杂矩阵代数和微分方程。本书形成于作者多年来给没有多少微积分知识的学生授课时所用的讲义，它假定读者预先没有编程的经验，并说明了智能系统中的大部分基础知识实际上是简单易懂的。本书目前已经被国际上多所大学（例如，德国的马德堡大学、日本的广岛大学、美国的波士顿大学和罗切斯特理工学院）采用。如果你正在寻找关于人工智能或智能系统设计课程的浅显易懂的入门级教材，如果你不是计算机科学领域的专业人员，而又正在寻找介绍基于知识系统最新技术发展的自学指南，本书将是最佳选择。本书的主要内容：基于规则的专家系统；模糊专家系统；基于框架的专家系统；人工神经网络；进化计算；混合智能系统；知识工程；数据挖掘。
作者简介
Michaelnegnevitsky澳大利亚塔斯马尼亚大学电气工程和计算机科学系教授，他的许多研究课题都涉及人工智能和软计算，一直致力于电气工程，过程控制和环境工程中的、智能系统的开发和应用，他著有200多篇论文、两本书，并获得了四项发明专利。相关图书80X86汇编语言与计算机体系结构计算机体系结构：量化研究方法：第3版分布式系统概念设计调和分析导论（英文第三版）电力系统分析（英文版·第2版）面向计算机科学的数理逻辑：系统建模与推理（英文版·第2版）Java2专家导引（英文版·第3版）机器视觉教程（英文版）（含盘）支持向量机导论（英文版）电子设计自动化基础（英文版）Java程序设计导论（英文版·第5版）数据挖掘：实用机器学习技术（英文版·第2版）UML参考手册（第2版）Java教程（英文版·第2版）软件需求管理：用例方法（英文版·第2版）数字通信导论UML参考手册（英文版·第2版）计算理论导引实用软件工程（英文版）计算机取证（英文版）EffectiveC#（英文版）UNIX教程（英文版·第2版）软件测试（英文版第2版）设计模式精解（英文版第2版）Linux内核编程必读-经典原版书库实分析和概率论-经典原版书库（英文版.第2版）软件过程改进（英文版）计算机科学概论（英文版·第2版）数学规划导论英文版抽样理论与方法（英文版）复分析基础及工程应用（英文版.第3版）离散事件系统仿真（英文版·第4版）复杂SoC设计（英文版）基于FPGA的系统设计（英文版）基于用例的面向方面软件开发（英文版）
目录
Preface
Preface to the Second edition
Acknowledgements
1 Introduction To Knowledge-Based Intelligent Systems
1.1 Intelligent Machines, Or What Machines Can Do
1.2 The History Of Artificial Intelligence, Or From The‘DarkAges’To Knowledge-Based Systems
1.3 Summary
Questions For Review
References
2 Rule-Based Expert Systems
2.1 Introduction, Or What Is Knowledge?
2.2 Rules As A Knowledge Representation Technique
2.3 The Main Players In The Expert System Development Team
2.4 Structure Of A Rule-Based Expert System
2.5 Fundamental Characteristics Of An Expert System
2.6 Forward Chaining And Backward Chaining Inference Techniques
2.7 MEDIA ADVISOR: A Demonstration Rule-Based Expert System
2.8 Conflict Resolution
2.9 Advantages And Disadvantages Of Rule-Based Expert Systems
2.10 Summary
Questions For Review
References
3 Uncertainty Management In Rule-Based Expert Systems
3.1 Introduction, Or What Is Uncertainty?
3.2 Basic Probability Theory
3.3 Bayesian Reasoning
3.4 FORECAST: Bayesian Accumulation Of Evidence
3.5 Bias Of The Bayesian Mesod
3.6 Certainty Factors Theory And Evidential Reasoning
3.7 FORECAST: An Application Of Certainty Factors
3.8 Comparison Of Bayesian Reasoning And Certainty Factors
3.9 Summary
Questions For Review
References
4 Fuzzy Expert Systems
4.1 Introduction, Or What Is Fuzzy Thinking?
4.2 Fuzzy Sets
4.3 Linguistic Variables And Hedges
4.4 Operations Of Fuzzy Sets
4.5 Fuzzy Rules
4.6 Fuzzy Inference
4.7 Building A Fuzzy Expert System
4.8 Summary
Questions For Review
References
Bibliography
5 Frame-Based Expert Systems
5.1 Introduction, Or What Is A Frame?
5.2 Frames As A Knowledge Representation Technique
5.3 Inference In Frame-Based Experts
5.4 Methods And Demons
5.5 Interaction Of Frames And Rules
5.6 Buy Smart: A Frame-Based Expert System
5.7 Summary
Questions For Review
References
Bibliography
6 Artificial Neural Networks
6.1 Introduction, Or How The Brain Works
6.2 The Neuron As A Simple Computing Element
6.3 The Perceptron
6.4 Multilayer Neural Networks
6.5 Accelerated Learning In Multilayer Neural Networks
6.6 The Hopfield Network
6.7 Bidirectional Associative Memories
6.8 Self-Organising Neural Networks
6.9 Summary
Questions For Review
References
7 Evolutionary Computation
7.1 Introduction, Or Can Evolution Be Intelligent?
7.2 Simulation Of Natural Evolution
7.3 Genetic Algorithms
7.4 Why Genetic Algorithms Work
7.5 Case Study: Maintenance Scheduling With Genetic Algorithms
7.6 Evolutionary Strategies
7.7 Genetic Programming
7.8 Summary
Questions For Review
References
8 Hybrid Intelligent Systems
8.1 Introduction, Or How To Combine German Mechanics With Italian Love
8.2 Neural Expert Systems
8.3 Neuro-Fuzzy Systems
8.4 ANFIS: Adaptive Neuro-Fuzy Inference System
8.5 Evolutionary Neural Networks
8.6 Fuzzy Evolutionary Systems
8.7 Summary
Questions For Review
References
9 Knowledge Engineering And Data Mining
9.1 Introduction, Or What Is Knowledge Engineering?
9.2 Will An Expert System Work For My Problem?
9.3 Will A Fuzzy Expert System Work For My Problem?
9.4 Will A Neural Network Work For My Problem?
9.5 Will Genetic Algorithms Work For My Problem?
9.6 Will A Neuro-Fuzzy System Work For My Problem?
9.7 Data Mining And Knowledge Discovery
9.8 Summary
Questions For Review
References
Glossary
Appendix
Index
Preface to the Second edition
Acknowledgements
1 Introduction To Knowledge-Based Intelligent Systems
1.1 Intelligent Machines, Or What Machines Can Do
1.2 The History Of Artificial Intelligence, Or From The‘DarkAges’To Knowledge-Based Systems
1.3 Summary
Questions For Review
References
2 Rule-Based Expert Systems
2.1 Introduction, Or What Is Knowledge?
2.2 Rules As A Knowledge Representation Technique
2.3 The Main Players In The Expert System Development Team
2.4 Structure Of A Rule-Based Expert System
2.5 Fundamental Characteristics Of An Expert System
2.6 Forward Chaining And Backward Chaining Inference Techniques
2.7 MEDIA ADVISOR: A Demonstration Rule-Based Expert System
2.8 Conflict Resolution
2.9 Advantages And Disadvantages Of Rule-Based Expert Systems
2.10 Summary
Questions For Review
References
3 Uncertainty Management In Rule-Based Expert Systems
3.1 Introduction, Or What Is Uncertainty?
3.2 Basic Probability Theory
3.3 Bayesian Reasoning
3.4 FORECAST: Bayesian Accumulation Of Evidence
3.5 Bias Of The Bayesian Mesod
3.6 Certainty Factors Theory And Evidential Reasoning
3.7 FORECAST: An Application Of Certainty Factors
3.8 Comparison Of Bayesian Reasoning And Certainty Factors
3.9 Summary
Questions For Review
References
4 Fuzzy Expert Systems
4.1 Introduction, Or What Is Fuzzy Thinking?
4.2 Fuzzy Sets
4.3 Linguistic Variables And Hedges
4.4 Operations Of Fuzzy Sets
4.5 Fuzzy Rules
4.6 Fuzzy Inference
4.7 Building A Fuzzy Expert System
4.8 Summary
Questions For Review
References
Bibliography
5 Frame-Based Expert Systems
5.1 Introduction, Or What Is A Frame?
5.2 Frames As A Knowledge Representation Technique
5.3 Inference In Frame-Based Experts
5.4 Methods And Demons
5.5 Interaction Of Frames And Rules
5.6 Buy Smart: A Frame-Based Expert System
5.7 Summary
Questions For Review
References
Bibliography
6 Artificial Neural Networks
6.1 Introduction, Or How The Brain Works
6.2 The Neuron As A Simple Computing Element
6.3 The Perceptron
6.4 Multilayer Neural Networks
6.5 Accelerated Learning In Multilayer Neural Networks
6.6 The Hopfield Network
6.7 Bidirectional Associative Memories
6.8 Self-Organising Neural Networks
6.9 Summary
Questions For Review
References
7 Evolutionary Computation
7.1 Introduction, Or Can Evolution Be Intelligent?
7.2 Simulation Of Natural Evolution
7.3 Genetic Algorithms
7.4 Why Genetic Algorithms Work
7.5 Case Study: Maintenance Scheduling With Genetic Algorithms
7.6 Evolutionary Strategies
7.7 Genetic Programming
7.8 Summary
Questions For Review
References
8 Hybrid Intelligent Systems
8.1 Introduction, Or How To Combine German Mechanics With Italian Love
8.2 Neural Expert Systems
8.3 Neuro-Fuzzy Systems
8.4 ANFIS: Adaptive Neuro-Fuzy Inference System
8.5 Evolutionary Neural Networks
8.6 Fuzzy Evolutionary Systems
8.7 Summary
Questions For Review
References
9 Knowledge Engineering And Data Mining
9.1 Introduction, Or What Is Knowledge Engineering?
9.2 Will An Expert System Work For My Problem?
9.3 Will A Fuzzy Expert System Work For My Problem?
9.4 Will A Neural Network Work For My Problem?
9.5 Will Genetic Algorithms Work For My Problem?
9.6 Will A Neuro-Fuzzy System Work For My Problem?
9.7 Data Mining And Knowledge Discovery
9.8 Summary
Questions For Review
References
Glossary
Appendix
Index
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