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. Language: English