Table of Contents

1 Introduction.- 1.1 Neural information processing systems.- 1.2 ANNs for modelling and control.- 1.3 Chapter by Chapter overview.- 1.4 Contributions.- 2 Artificial neural networks: architectures and learning rules.- 2.1 Basic neural network architectures.- 2.2 Universal approximation theorems.- 2.3 Classical paradigms of learning.- 2.4 Conclusion.- 3 Nonlinear system identification using neural networks.- 3.1 From linear to nonlinear dynamical models.- 3.2 Parametrization by ANNs.- 3.3 Learning algorithms.- 3.4 Elements from nonlinear optimization theory.- 3.5 Aspects of model validation, pruning and regularization.- 3.6 Neural network models as uncertain linear systems.- 3.7 Examples.- 3.8 Conclusion.- 4 Neural networks for control.- 4.1 Neural control strategies.- 4.2 Neural optimal control.- 4.3 Conclusion.- 5 NLq Theory.- 5.1 A neural state space model framework for neural control design.- 5.2 NLq systems.- 5.3 Global asymptotic stability criteria for NLqs.- 5.4 Input/Output properties — l2 theory.- 5.5 Robust performance problem.- 5.6 Stability analysis: formulation as LMI problems.- 5.7 Neural control design.- 5.8 Control design: some case studies.- 5.9 NLqs beyond control.- 5.10 Conclusion.- 6 General conclusions and future work.- A.1 A generalization of Chua’s circuit.- B Fokker-Planck Learning Machine for Global Optimization.- B.1 Fokker-Planck equation for recursive stochastic algorithms.- B.2 Parametrization of the pdf by RBF networks.- B.3 FP machine: conceptual algorithm.- B.4 Examples.- B.5 Conclusions.