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Author:   Kevin L. Moore
Publisher:   Springer London Ltd
Imprint:   Springer London Ltd
Edition:   Softcover reprint of the original 1st ed. 1993
Dimensions:   Width: 15.50cm , Height: 0.90cm , Length: 23.50cm
Weight:   0.272kg
ISBN:  

9781447119142

ISBN 10:   1447119142
Pages:   152
Publication Date:   12 December 2011
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

1 Introduction to the Monograph.- 1.1 Background and Motivation: Transient Response Control.- 1.2 Organization of the Monograph.- 2 Iterative Learning Control: An Overview.- 2.1 Introduction.- 2.2 Literature Review.- 2.3 Problem Formulation.- 3 Linear Time-Invariant Learning Control.- 3.1 Convergence with Zero Error.- 3.2 Convergence with Non-Zero Error.- 3.3 The Nature of the Solution.- 4 LTI Learning Control via Parameter Estimation.- 4.1 System Description.- 4.2 Main Result.- 4.3 Comments.- 5 Finite-Horizon Learning Control.- 5.1 l?-Optimal Learning Control with Memory.- 5.2 Learning Convergence in One Step.- 5.3 Learning Control with Multirate Sampling.- 5.4 Examples.- 5.5 Comments and Extensions.- 6 Nonlinear Learning Control.- 6.1 Learning Control for Nonlinear Systems.- 6.2 Learning Controller for a Class of Nonlinear Systems.- 7 Artificial Neural Networks for Iterative Learning Control.- 7.1 Neural Network Controllers.- 7.2 Static Learning Controller Using an ANN.- 7.3 Dynamical Learning Controller Using an ANN.- 7.4 Reinforcement Learning Controller Using an ANN.- 8 Conclusion.- 8.1 Summary.- 8.2 Directions for Future Research.- Appendix A: Some Basic Results on Multirate Sampling.- A.1 Introduction.- A.3 Basic Result.- Appendix B: Tutorial on Artificial Neural Networks.- B.1 An Introduction to Neural Networks.- B.1.1 Neurons.- B.1.2 Interconnection Topology.- B.1.3 Learning Laws.- B.2 Historical Background.- B.3 Properties of Neural Networks.- B.3.1 Pattern Classification and Associative Memory.- B.3.2 Self-Organization and Feature Extraction.- B.3.3 Optimization.- B.3.4 Nonlinear Mappings.- B.4 Neural Nets and Computers.- B.5 Derivation of Backpropagation.- B.6 Neural Network References.- References.

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