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Artificial intelligence : a guide to intelligent systems

Author: Michael Negnevitsky
Publisher: Harlow, England ; New York : Addison Wesley, 2002.
Edition/Format:   Print book : English : 1st edView all editions and formats
Summary:

An introduction to artificial intelligence, covering expert systems, artificial neural networks, fuzzy systems and evolutionary computation. The author explains the principles and demonstrates how  Read more...

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Genre/Form: Einführung
Document Type: Book
All Authors / Contributors: Michael Negnevitsky
ISBN: 0201711591 9780201711592
OCLC Number: 51681952
Description: xiv, 394 pages : illustrations ; 25 cm
Contents: 1 Introduction to knowledge-based intelligent systems 1 --
1.1 Intelligent machines, or what machines can do 1 --
1.2 History of artificial intelligence, or from the 'Dark Ages' to knowledge-based systems 4 --
2 Rule-based expert systems 25 --
2.1 Introduction, or what is knowledge? 25 --
2.2 Rules as a knowledge representation technique 26 --
2.3 Main players in the expert system development team 28 --
2.4 Structure of a rule-based expert system 30 --
2.5 Fundamental characteristics of an expert system 33 --
2.6 Forward chaining and backward chaining inference techniques 35 --
2.7 THERMOSTAT: a demonstration rule-based expert system 41 --
2.8 Conflict resolution 46 --
2.9 Advantages and disadvantages of rule-based expert systems 49 --
3 Uncertainty management in rule-based expert systems 55 --
3.1 Introduction, or what is uncertainty? 55 --
3.2 Basic probability theory 57 --
3.3 Bayesian reasoning 61 --
3.4 FORECAST: Bayesian accumulation of evidence 65 --
3.5 Bias of the Bayesian method 72 --
3.6 Certainty factors theory and evidential reasoning 74 --
3.7 FORECAST: an application of certainty factors 80 --
3.8 Comparison of Bayesian reasoning and certainty factors 82 --
4 Fuzzy expert systems 87 --
4.1 Introduction, or what is fuzzy thinking? 87 --
4.2 Fuzzy sets 89 --
4.3 Linguistic variables and hedges 94 --
4.4 Operations of fuzzy sets 97 --
4.5 Fuzzy rules 103 --
4.6 Fuzzy inference 106 --
4.7 Building a fuzzy expert system 114 --
5 Frame-based expert systems 129 --
5.1 Introduction, or what is a frame? 129 --
5.2 Frames as a knowledge representation technique 131 --
5.3 Inheritance in frame-based systems 136 --
5.4 Methods and demons 140 --
5.5 Interaction of frames and rules 144 --
5.6 Buy Smart: a frame-based expert system 147 --
6 Artificial neural networks 163 --
6.1 Introduction, or how the brain works 163 --
6.2 Neuron as a simple computing element 166 --
6.3 Perceptron 168 --
6.4 Multilayer neural networks 173 --
6.5 Accelerated learning in multilayer neural networks 183 --
6.6 Hopfield network 186 --
6.7 Bidirectional associative memory 194 --
6.8 Self-organising neural networks 198 --
7 Evolutionary computation 217 --
7.1 Introduction, or can evolution be intelligent? 217 --
7.2 Simulation of natural evolution 217 --
7.3 Genetic algorithms 220 --
7.4 Why genetic algorithms work 230 --
7.5 Case study: maintenance scheduling with genetic algorithms 233 --
7.6 Evolution strategies 240 --
7.7 Genetic programming 243 --
8 Hybrid intelligent systems 257 --
8.1 Introduction, or how to combine German mechanics with Italian love 257 --
8.2 Neural expert systems 259 --
8.3 Neuro-fuzzy systems 266 --
8.4 ANFIS: Adaptive Neuro-Fuzzy Inference System 275 --
8.5 Evolutionary neural networks 283 --
8.6 Fuzzy evolutionary systems 288 --
9 Knowledge engineering and data mining 299 --
9.1 Introduction, or what is knowledge engineering? 299 --
9.2 Will an expert system work for my problem? 306 --
9.3 Will a fuzzy expert system work for my problem? 315 --
9.4 Will a neural network work for my problem? 321 --
9.5 Data mining and knowledge discovery 330.
Responsibility: Michael Negnevitsky.

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