Recent Advances in Intelligent Paradigms and Applications

Front Cover
Ajith Abraham
Springer Science & Business Media, Nov 26, 2002 - Computers - 272 pages
Digital systems that bring together the computing capacity for processing large bodies of information with the human cognitive capability are called intelligent systems. Building these systems has become one of the great goals of modem technology. This goal has both intellectual and economic incentives. The need for such intelligent systems has become more intense in the face of the global connectivity of the internet. There has become an almost insatiable requirement for instantaneous information and decision brought about by this confluence of computing and communication. This requirement can only be satisfied by the construction of innovative intelligent systems. A second and perhaps an even more significant development is the great advances being made in genetics and related areas of biotechnology. Future developments in biotechnology may open the possibility for the development of a true human-silicon interaction at the micro level, neural and cellular, bringing about a need for "intelligent" systems. What is needed to further the development of intelligent systems are tools to enable the representation of human cognition in a manner that allows formal manipulation. The idea of developing such an algebra goes back to Leibniz in the 17th century with his dream of a calculus ratiocinator. It wasn't until two hundred years later beginning with the work of Boole, Cantor and Frege that a formal mathematical logic for modeling human reasoning was developed. The introduction of the modem digital computer during the Second World War by von Neumann and others was a culmination of this intellectual trend.
 

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Contents

Architectures and Perspectives
1
2 Models Of Hybrid Soft Computing Architectures
4
22 Transformational Hybrid Intelligent System
5
24 Integrated Intelligent System
6
31 Meta Learning Evolutionary Artificial Neural Networks
8
4 Adaptation of Fuzzy Inference Systems
12
5 Evolutionary Fuzzy Systems
14
6 Cooperative NeuroFuzzy Systems
15
7 Conclusions
154
References
156
NeuroFuzzy Methods for Modeling and Identification
161
21 Nonlinear System Identification
162
23 Artificial Neural Networks
166
3 NeuroFuzzy Modeling
172
31 Constructing NeuroFuzzy Networks
174
33 GradientBased Learning
175

61 Fuzzy Associative Memories
16
62 Fuzzy Rule Extraction Using Self Organizing Maps
17
63 Systems Capable of Learning Fuzzy Set Parameters
18
7 Integrated NeuroFuzzy Systems
19
71 Integrated NeuroFuzzy System Mamdani FIS
20
72 Integrated Neurofuzzy system TakagiSugeno FIS
21
8 NeuroFuzzyEvolutionary EvoNF Systems
24
9 Fuzzy Evolutionary Algorithms
26
1O Soft Computing and Probabilistic Reasoning
27
Acknowledgements
28
Hybrid Architecture for Autonomous Robots Based on Representation Perception and Intelligent Control
37
11 Autonomy in robotic systems
38
12 Robot control architectures
39
2 HARPIC
40
22 Management of perception resources
42
23 Assessment mechanisms within the control architecture
43
24 Comparison with other architectures
47
25 Implementation and experiments
49
3 Computational intelligence and controlled autonomy
53
4 Computational intelligence and learning
54
5 Conclusion
55
An Application to Medical Diagnosis
57
2 Brief Introduction to Intuitionistic Fuzzy Sets
59
2 1 Distances Between Intuitionistic Fuzzy Sets
61
3 An Intuitionistic Fuzzy Sets Approach to Medical Diagnosis due to De Biswas and Roy
64
4 Medical Diagnosis via Distances for Intuitionistic Fuzzy Sets
67
5 Conclusions
69
References
70
A Fuzzy Inference Methodology Based on the Fuzzification of Set Inclusion
71
2 Classical Inference Strategies
73
3 InclusionBased Approach
79
32 InclusionBased Reasoning with One Fuzzy Rule
82
33 InclusionBased Reasoning with Parallel Fuzzy Rules
83
4 Conclusion
87
5 Acknowledgements
88
A Fuzzy Approach to JobShop Scheduling Problem Based on Imprecise Processing Times
91
2 The JobShop Scheduling Problem
93
22 Fuzzy Job Processing Times
94
3 Preliminaries
95
4 Fuzzy Job Shop Scheduling Model Based on Imprecise Processing Times
98
5 Computational Results
101
6 Concluding Remarks
104
References
105
Towards an Optimal Combination of Granularity and HigherOrder Approaches
107
Case Study
111
3 Selecting Operations that Are in Optimal Agreement with Granularity
116
4 Optimal Selection of HigherOrder Approach
122
Preliminary Results
126
6 Conclusions
128
Acknowledgments
129
Discovering Efficient Learning Rules for Feedforward Neural Networks Using Genetic Programming
133
2 Standard Backpropagation Algorithm and Recent Improvements
135
22 Improvements to SBP
136
3 Previous Work on the Evolution of Neural Network Learning Rules
138
4 Our Approach to Evolving Learning Rules with GP
140
Learning Rules for Output Layers
141
Learning Rules for Hidden Layers
143
6 Discussion
151
35 Initialization of Antecedent Membership Functions
179
4 Simulation Examples
181
42 pH Neutralization Process
183
5 Concluding Remarks
185
References
186
Constrained Two Dimensional Bin Packing Using a Genetic Algorithm
187
2 Some Industrial Applications of 2Dimensional Bin Packing
189
23 Packing
190
3 A Brief Description of Genetic Algorithm
191
33 Selection
192
34 Crossover
193
35 Mutation
194
4 Proposed Genetic Algorithm for TwoDimensional Packing
195
41 Model Representation
196
42 Objective Function
197
45 Mutation
198
452 Orientation
199
47 Constraint Handling
200
5 Performance Evaluation of TwoDimensional Genetic Algorithm
201
52 Comparison With Another Genetic Algorithm
205
6 Conclusion
208
Cargo Details
210
Sequential and Distributed Evolutionary Algorithms for Combinatorial Optimization Problems
211
2 The Evolutionary Algorithms
212
21 Sequential Evolutionary Algorithms
214
22 Distributed Evolutionary Algorithms
215
3 Combinatorial Optimization Problems
216
31 The Maximum Cut Problem
217
32 The Error Correcting Code Design Problem
219
33 The Minimum Tardy Task Problem
221
4 Experimental Runs
224
41 Results for the Maximum Cut Problem
225
42 Results for the ECC Problem
226
43 Results for the Minimum Tardy Task Problem
228
5 Conclusion
230
References
232
Embodied Emotional Agent in Intelligent Training System
235
2 The problem of emotion generation
237
23 Models of architecture for emotion generation
239
3 Producing emotions by qualitative reasoning
240
31 Qualitative reasoning
241
33 Emotion generation based on a qualitative reasoning system
243
41 Operational context
244
42 Architecture overview
245
43 Choosing and treating the input
246
44 Generating emotions
248
45 Providing visual feedback from emotions
249
5 Discussions and conclusion
250
6 References
251
Optimizing Intelligent Agents Constraint Satisfaction with Neural Networks
255
2 Preparing the Input for the Neural Networks
257
3 Design of Neural Network
259
32 Neural Network Selection and Criteria
260
33 Learning Algorithms for FFNN
261
4 Data Analysis and Results
262
5 Conclusion
270
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