人工智能：智能系统指南

图书标准信息

• 作者 [澳]Michael Negnevitsky 著
• 出版社 机械工业出版社
• 出版时间 2011-09
• 版次 3
• ISBN 9787111358220
• 定价 49.00元
• 装帧 平装
• 开本 32开
• 纸张 胶版纸
• 页数 479页
• 原版书名 Artificial Intelligence: A Guide to Intelligent Systems, Third Edition
• 丛书 经典原版书库

【内容简介】

人工智能经常被人们认为是计算机科学中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。本书基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，假定读者没有编程经验，以简单易懂的方式介绍了智能系统的基础知识。
《人工智能智能系统指南（英文版.第3版）》目前已经被国际上多所大学（例如，德国的马格德堡大学、日本的广岛大学、美国的波士顿大学和罗切斯特理工学院等）采纳为教材。
如果您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，如果您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，本书将是您的最佳选择。
与上一版相比，本版进行了全面更新，以反映人工智能领域的最新进展。其中新增了数据挖掘与知识发现一章和自组织神经网络聚类一节内容，同时补充了4个新的案例研究。

【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。他一直致力于电气工程、过程控制和环境工程中智能系统的开发和应用，著有200多篇论文、两本专著，并获得了四项发明专利。

【目录】

preface
prefacetothethirdedition
overviewofthebook
acknowledgements
1introductiontoknowledge-basedintelligentsystems
1.1intelligentmachines，orwhatmachinescando
1.2thehistoryofartificialintelligence，orfromthe'darkages'toknowledge*basedsystems
1.3summary
questionsforreview
references

2rule-basedexpertsystems
2.1introduction，orwhatisknowledge?
2.2rulesasaknowledgerepresentationtechnique
2.3themainplayersintheexpertsystemdevelopmentteam
2.4structureofarule*basedexpertsystem
2.5fundamentalcharacteristicsofanexpertsystem
2.6forwardchainingandbackwardchaininginferencetechniques
2.7mediaadvisor:ademonstrationrule*basedexpertsystem
2.8conflictresolution
2.9advantagesanddisadvantagesofrule*basedexpertsystems
2.10summary
questionsforreview
references

3uncertaintymanagementinrule-basedexpertsystems
3.1introduction，orwhatisuncertainty?
3.2basic.probabilitytheory
3.3bayesianreasoning
3.4forecast:bayesianaccumulationofevidence
3.5biasofthebayesianmethod
3.6certaintyfactorstheoryandevidentialreasoning
3.7forecast:anapplicationofcertaintyfactors
3.8comparisonofbayesianreasoningandcertaintyfactors
3.9summary
questionsforreview
references

4fuzzyexpertsystems
4.1introduction，orwhatisfuzzythinking?
4.2fuzzysets
4.3linguisticvariablesandhedges
4.4operationsoffuzzysets
4.5fuzzyrules
4.6fuzzyinference
4.7buildingafuzzyexpertsystem
4.8summary
questionsforreview
references
bibliography

5frame-basedexpertsystems
5.1introduction，orwhatisaframe?
5.2framesasaknowledgerepresentationtechnique
5.3inheritanceinframe-basedsystems
5.4methodsanddemons
5.5interactionofframesandrules
5.6buysmart:aframe-basedexpertsystem
5.7summary
questionsforreview
references
bibliography

6artificialneuralnetworks
6.1introduction，orhowthebrainworks
6.2theneuronasasimplecomputingelement
6.3theperceptron
6.4multilayerneuralnetworks
6.5acceleratedlearninginmultilayerneuralnetworks
6.6thehopfieldnetwork
6.7bidirectionalassociativememory
6.8self-organisingneuralnetworks
6.9summary
questionsforreview
references
evolutionarycomputation

7.1introduction，orcanevolutionbeintelligent?
7.2simulationofnaturalevolution
7.3geneticalgorithms
7.4whygeneticalgorithmswork
7.5casestudy:maintenanceschedulingwithgeneticalgorithms
7.6evolutionstrategies
7.7geneticprogramming
7.8summary
questionsforreview
references
bibliography

8hybridintelligentsystems
8.1introduction，orhowtocombinegermanmechanicswithitalianlove
8.2neuralexpertsystems
8.3neuro-fuzzysystems
8.4anfis:adaptiveneuro-fuzzyinferencesystem
8.5evolutionaryneuralnetworks
8.6fuzzyevolutionarysystems
8.7summary
questionsforreview
references
9knowledgeengineering

9.1introduction，orwhatisknowledgeengineering?
9.2willanexpertsystemworkformyproblem?
9.3willafuzzyexpertsystemworkformyproblem?
9.4willaneuralnetworkworkformyproblem?
9.5willgeneticalgorithmsworkformyproblem?
9.6willahybridintelligentsystemworkformyproblem?
9.7summary
questionsforreview
references

10dataminingandknowledgediscovery
10.1introduction，orwhatisdatamining?
10.2statisticalmethodsanddatavisualisation
10.3principalcomponentanalysis
10.4relationaldatabasesanddatabasequeries
10.sthedatawarehouseandmultidimensionaldataanalysis
10.6decisiontrees
10.7associationrulesandmarketbasketanalysis
10.8summary
questionsforreview
references
glossary
appendix:altoolsandvendors
index