Computational Intelligence
The Experts Speak
1st Edition
Published on 24. June 2003
Book
Hardback
304 pages
978-0-471-27454-4 (ISBN)
Description
* Computational intelligence is the term used by computer scientists to describe the overlapping technical interest areas that would be more readily identified by nontechnologists as AI
* Includes past, present, and future examples of applications developed from computational intelligence studies
* The editors and contributors are the leaders in the CI community writing in their respective fields of expertise
Reviews / Votes
"...this volume provides a valuable coherent snapshot of computational intelligence and its applications." (Journal of Intelligent & Fuzzy Systems, July 2004)More details
Product info
GB
Edition
1., Auflage
Language
English
Place of publication
United States
Publishing group
John Wiley & Sons Inc
Target group
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Illustrations
Drawings: 85 B&W, 0 Color
Dimensions
Height: 240 mm
Width: 161 mm
Thickness: 21 mm
Weight
621 gr
ISBN-13
978-0-471-27454-4 (9780471274544)
Schweitzer Classification
Persons
David B. Fogel, PhD, is the Chief Executive Officer of Natural Selection Inc. and was the general chairman of the 2002 IEEE World Congress on Computational Intelligence.
Charles J. Robinson, DSc, PE, is Director of the University Center for Biomedical Engineering and Rehabilitation Science at Louisiana Tech University and a Senior Rehabilitation Research Career Scientist with the Shreveport, LA, Veterans Affairs Medical Center. He served as plenary chairman of the 2002 IEEE World Congress on Computational Intelligence.
Content
CONTRIBUTORS. PREFACE.
1. THREE GENERATIONS OF COEVOLUTIONARY ROBOTICS (Jordan B. Pollack, Hod Lipson, Pablo Funes, and Gregory Hornby).
1.1 Roboeconomics.
1.2 Coevolution.
1.3 Research Thrusts.
1.4 Conclusion.
Acknowledgments.
References.
2. BEYOND 2001: THE LINGUISTIC SPATIAL ODYSSEY (James M. Keller, Pascal Matsakis, and Marjorie Skubic).
2.1 Introduction.
2.2 Force Histograms and Linguistic Scene Description.
2.3 Scene Matching.
2.4 Human-Robot Dialog.
2.5 Sketched Route Map Understanding.
2.6 The Future.
Acknowledgments.
References.
3. COMPUTING MACHINERY AND INTELLIGENCE AMPLIFICATION (Steven K. Rogers, Matthew Kabrisky, Kenneth Bauer, and Mark E. Oxley).
3.1 Introduction.
3.2 Estimating Intelligence.
3.3 Turing Test and Intelligence Amplification.
3.4 Measuring Intelligence Amplification.
3.5 The Future of Intelligence Amplification.
References.
4. VISUALIZING COMPLEXITY IN THE BRAIN (Lloyd Watts).
4.1 Introduction.
4.2 Neuroscience Knowledge.
4.3 Computing Technology.
4.4 Nontechnical Issues.
4.5 Conclusions.
References.
5. EMERGING TECHNOLOGIES: ONR'S NEED FOR INTELLIGENT COMPUTATION IN UNDERWATER SENSORS (James F. McEachern and Robert T. Miyamoto).
5.1 Introduction.
5.2 Background.
5.3 The Challenge.
5.4 Current Applications.
5.5 Future.
5.6 Summary.
References.
6. BEYOND VOLTERRA AND WIENER: OPTIMAL MODELING OF NONLINEAR DYNAMICAL SYSTEMS IN A NEURAL SPACE FOR APPLICATIONS IN COMPUTATIONAL INTELLIGENCE (Rui J. P. de Figueiredo).
6.1 Introduction.
6.2 Classes of Nonlinear Dynamical System Models.
6.3 The de Figueiredo-Dwyer-Zyla Space F.
6.4 Derivation of Sigmoid Functionals.
6.5 Best Robust Approximation of f in the Neural Space N.
6.6 Optimal Combined Structural and Parametric Modeling of Nonlinear Dynamical Systems in N.
6.7 Computationally Intelligent (CI) Systems.
6.8 Concluding Remarks.
References.
7. TECHNIQUES FOR EXTRACTING CLASSIFICATION AND REGRESSION RULES FROM ARTIFICIAL NEURAL NETWORKS (Rudy Setiono).
7.1 Introduction.
7.2 Rule Extraction.
7.3 Illustrative Examples.
7.4 Conclusion.
References.
8. NEURAL NETWORKS FOR CONTROL: RESEARCH OPPORTUNITIES AND RECENT DEVELOPMENTS (Paul J. Werbos).
8.1 The Challenge to Researchers: Context and Motivation.
8.2 A Specific Challenge and Associated Issues.
8.3 Strategies, Tasks, and Tools.
References.
9. INTELLIGENT LEARNING ROBOTIC SYSTEMS USING COMPUTATIONAL INTELLIGENCE (Toshio Fukuda and Naoyuki Kubota).
9.1 Introduction.
9.2 Motion Planning and Behavior Acquisition of Robots.
9.3 Emerging Synthesis of Computational Intelligence.
9.4 Intelligence on Robotic Systems.
9.5 Structured Intelligence for Robotic Systems.
9.6 Concluding Remarks.
References.
10. COMPUTATIONAL INTELLIGENCE IN LOGISTICS (Hans-Juergen Zimmermann).
10.1 Introduction.
10.2 Traffic Management.
10.3 Fleet Management.
10.4 In-House Logistics.
10.5 Conclusions.
References.
11. TWO NEW CONVERGENCE RESULTS FOR ALTERNATING OPTIMIZATION (James C. Bezdek and Richard J. Hathaway).
11.1 Iterative Optimization.
11.2 Existence and Uniqueness.
11.3 The Alternating Optimization Algorithm.
11.4 When Is Alternating Optimization a Good Choice?
11.5 How Do We Solve (11.1)?
11.6 Local Convergence of Alternating Optimization.
11.7 Global Convergence of AO.
11.8 Conclusions.
Acknowledgment.
References.
12. CONSTRUCTIVE DESIGN OF A DISCRETE-TIME FUZZY CONTROLLER BASED ON PIECEWISE-LYAPUNOV FUNCTIONS (Gang Feng, Dong Sun, and Louis Wang).
12.1 Introduction.
12.2 Fuzzy Dynamic Model and Its Piecewise-Quadratic Stability.
12.3 Controller Synthesis of Fuzzy Dynamic Systems.
12.4 Simulation Examples.
12.5 Conclusions.
Acknowledgments.
References.
Appendix.
13. EVOLUTIONARY COMPUTATION AND COGNITIVE SCIENCE (Janet Wiles and Jennifer Hallinan).
13.1 Cognitive Science: What's on Your Mind?
13.2 Case Studies in Evolutionary Computation and Cognitive Science.
13.3 Summary.
References.
14. EVOLVABLE HARDWARE AND ITS APPLICATIONS (T. Higuchi, E. Takahashi, Y. Kasai, T. Itatani, M. Iwata, H. Sakanashi, M. Murakawa, I. Kajitani, and H. Nosato).
14.1 Introduction.
14.2 Myoelectric Prosthetic Hand Controller with EHW.
14.3 Data-Compression Chip for Printing Image Data.
14.4 Analog EHW Chip for Cellular Phone.
14.5 An EHW-Based Clock-Timing Adjusting Chip.
14.6 Evolvable Optical Systems and Their Application.
14.7 Current Research on EHW.
References.
15. HUMANIZED COMPUTATIONAL INTELLIGENCE WITH INTERACTIVE EVOLUTIONARY COMPUTATION (Hideyuki Takagi).
15.1 Introduction.
15.2 Humanized Computational Intelligence.
15.3 Interactive Evolutionary Computation.
15.4 Conclusion.
References.
16. UNSUPERVISED LEARNING BY ARTIFICIAL NEURAL NETWORKS (Harold Szu).
16.1 A New Challenge: Space-Variant Unsupervised Classifications.
16.2 Power of Pairs: Vector versus Scalar Data.
16.3 Generalization of Shannon's Entropy Information Theory to Open Systems.
16.4 Benchmarks of Space-Variant Unsupervised Classification.
16.5 Multispectral Medical Imaging.
16.6 Multispectral Remote Sensing.
16.7 Biological Relevance.
16.8 Conclusion.
Acknowledgments.
References.
17. COLLECTIVE INTELLIGENCE (David H. Wolpert).
17.1 Motivation and Background.
17.2 The Mathematics of Designing Collectives.
17.3 Tests of the Mathematics.
17.4 Conclusion.
References.
18. BACKPROPAGATION: GENERAL PRINCIPLES AND ISSUES FOR BIOLOGY (Paul J. Werbos).
18.1 Introduction.
18.2 The Chain Rule for Ordered Derivatives.
18.3 Backpropagation for Supervised Learning.
18.4 Discussion and Future Research.
References.
INDEX.
ABOUT THE EDITORS.
1. THREE GENERATIONS OF COEVOLUTIONARY ROBOTICS (Jordan B. Pollack, Hod Lipson, Pablo Funes, and Gregory Hornby).
1.1 Roboeconomics.
1.2 Coevolution.
1.3 Research Thrusts.
1.4 Conclusion.
Acknowledgments.
References.
2. BEYOND 2001: THE LINGUISTIC SPATIAL ODYSSEY (James M. Keller, Pascal Matsakis, and Marjorie Skubic).
2.1 Introduction.
2.2 Force Histograms and Linguistic Scene Description.
2.3 Scene Matching.
2.4 Human-Robot Dialog.
2.5 Sketched Route Map Understanding.
2.6 The Future.
Acknowledgments.
References.
3. COMPUTING MACHINERY AND INTELLIGENCE AMPLIFICATION (Steven K. Rogers, Matthew Kabrisky, Kenneth Bauer, and Mark E. Oxley).
3.1 Introduction.
3.2 Estimating Intelligence.
3.3 Turing Test and Intelligence Amplification.
3.4 Measuring Intelligence Amplification.
3.5 The Future of Intelligence Amplification.
References.
4. VISUALIZING COMPLEXITY IN THE BRAIN (Lloyd Watts).
4.1 Introduction.
4.2 Neuroscience Knowledge.
4.3 Computing Technology.
4.4 Nontechnical Issues.
4.5 Conclusions.
References.
5. EMERGING TECHNOLOGIES: ONR'S NEED FOR INTELLIGENT COMPUTATION IN UNDERWATER SENSORS (James F. McEachern and Robert T. Miyamoto).
5.1 Introduction.
5.2 Background.
5.3 The Challenge.
5.4 Current Applications.
5.5 Future.
5.6 Summary.
References.
6. BEYOND VOLTERRA AND WIENER: OPTIMAL MODELING OF NONLINEAR DYNAMICAL SYSTEMS IN A NEURAL SPACE FOR APPLICATIONS IN COMPUTATIONAL INTELLIGENCE (Rui J. P. de Figueiredo).
6.1 Introduction.
6.2 Classes of Nonlinear Dynamical System Models.
6.3 The de Figueiredo-Dwyer-Zyla Space F.
6.4 Derivation of Sigmoid Functionals.
6.5 Best Robust Approximation of f in the Neural Space N.
6.6 Optimal Combined Structural and Parametric Modeling of Nonlinear Dynamical Systems in N.
6.7 Computationally Intelligent (CI) Systems.
6.8 Concluding Remarks.
References.
7. TECHNIQUES FOR EXTRACTING CLASSIFICATION AND REGRESSION RULES FROM ARTIFICIAL NEURAL NETWORKS (Rudy Setiono).
7.1 Introduction.
7.2 Rule Extraction.
7.3 Illustrative Examples.
7.4 Conclusion.
References.
8. NEURAL NETWORKS FOR CONTROL: RESEARCH OPPORTUNITIES AND RECENT DEVELOPMENTS (Paul J. Werbos).
8.1 The Challenge to Researchers: Context and Motivation.
8.2 A Specific Challenge and Associated Issues.
8.3 Strategies, Tasks, and Tools.
References.
9. INTELLIGENT LEARNING ROBOTIC SYSTEMS USING COMPUTATIONAL INTELLIGENCE (Toshio Fukuda and Naoyuki Kubota).
9.1 Introduction.
9.2 Motion Planning and Behavior Acquisition of Robots.
9.3 Emerging Synthesis of Computational Intelligence.
9.4 Intelligence on Robotic Systems.
9.5 Structured Intelligence for Robotic Systems.
9.6 Concluding Remarks.
References.
10. COMPUTATIONAL INTELLIGENCE IN LOGISTICS (Hans-Juergen Zimmermann).
10.1 Introduction.
10.2 Traffic Management.
10.3 Fleet Management.
10.4 In-House Logistics.
10.5 Conclusions.
References.
11. TWO NEW CONVERGENCE RESULTS FOR ALTERNATING OPTIMIZATION (James C. Bezdek and Richard J. Hathaway).
11.1 Iterative Optimization.
11.2 Existence and Uniqueness.
11.3 The Alternating Optimization Algorithm.
11.4 When Is Alternating Optimization a Good Choice?
11.5 How Do We Solve (11.1)?
11.6 Local Convergence of Alternating Optimization.
11.7 Global Convergence of AO.
11.8 Conclusions.
Acknowledgment.
References.
12. CONSTRUCTIVE DESIGN OF A DISCRETE-TIME FUZZY CONTROLLER BASED ON PIECEWISE-LYAPUNOV FUNCTIONS (Gang Feng, Dong Sun, and Louis Wang).
12.1 Introduction.
12.2 Fuzzy Dynamic Model and Its Piecewise-Quadratic Stability.
12.3 Controller Synthesis of Fuzzy Dynamic Systems.
12.4 Simulation Examples.
12.5 Conclusions.
Acknowledgments.
References.
Appendix.
13. EVOLUTIONARY COMPUTATION AND COGNITIVE SCIENCE (Janet Wiles and Jennifer Hallinan).
13.1 Cognitive Science: What's on Your Mind?
13.2 Case Studies in Evolutionary Computation and Cognitive Science.
13.3 Summary.
References.
14. EVOLVABLE HARDWARE AND ITS APPLICATIONS (T. Higuchi, E. Takahashi, Y. Kasai, T. Itatani, M. Iwata, H. Sakanashi, M. Murakawa, I. Kajitani, and H. Nosato).
14.1 Introduction.
14.2 Myoelectric Prosthetic Hand Controller with EHW.
14.3 Data-Compression Chip for Printing Image Data.
14.4 Analog EHW Chip for Cellular Phone.
14.5 An EHW-Based Clock-Timing Adjusting Chip.
14.6 Evolvable Optical Systems and Their Application.
14.7 Current Research on EHW.
References.
15. HUMANIZED COMPUTATIONAL INTELLIGENCE WITH INTERACTIVE EVOLUTIONARY COMPUTATION (Hideyuki Takagi).
15.1 Introduction.
15.2 Humanized Computational Intelligence.
15.3 Interactive Evolutionary Computation.
15.4 Conclusion.
References.
16. UNSUPERVISED LEARNING BY ARTIFICIAL NEURAL NETWORKS (Harold Szu).
16.1 A New Challenge: Space-Variant Unsupervised Classifications.
16.2 Power of Pairs: Vector versus Scalar Data.
16.3 Generalization of Shannon's Entropy Information Theory to Open Systems.
16.4 Benchmarks of Space-Variant Unsupervised Classification.
16.5 Multispectral Medical Imaging.
16.6 Multispectral Remote Sensing.
16.7 Biological Relevance.
16.8 Conclusion.
Acknowledgments.
References.
17. COLLECTIVE INTELLIGENCE (David H. Wolpert).
17.1 Motivation and Background.
17.2 The Mathematics of Designing Collectives.
17.3 Tests of the Mathematics.
17.4 Conclusion.
References.
18. BACKPROPAGATION: GENERAL PRINCIPLES AND ISSUES FOR BIOLOGY (Paul J. Werbos).
18.1 Introduction.
18.2 The Chain Rule for Ordered Derivatives.
18.3 Backpropagation for Supervised Learning.
18.4 Discussion and Future Research.
References.
INDEX.
ABOUT THE EDITORS.