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Present years have seen a speedy enchancment of neural network control tech niques and their worthwhile functions. Fairly a number of simulation analysis and exact industrial implementations current that artificial neural network is an efficient candidate for carry out approximation and control system design in fixing the control problems with difficult nonlinear strategies in the presence of varied types of uncertainties. Many control approaches/methods, reporting improvements and control functions contained in the fields of adaptive control, neural control and fuzzy strategies, have been revealed in quite a few books, journals and conference proceedings. No matter these distinctive advances in neural control space, due to the complexity of nonlinear methods, the present evaluation on adaptive neural control continues to be focused on the occasion of elementary methodologies. From a theoretical viewpoint, there’s, in primary, lack of a firmly mathematical basis in stability, robustness, and effectivity analysis of neural network adaptive control strategies. This book is motivated by the need for systematic design approaches for safe adaptive control using approximation-based strategies. The first objec tives of the book are to develop safe adaptive neural control strategies, and to hold out transient effectivity analysis of the resulted neural control strategies analytically. Totally different linear-in-the-parameter carry out approximators can substitute the linear-in-the-parameter neural networks in the controllers launched in the book with none drawback, which embrace polynomials, splines, fuzzy strategies, wavelet networks, amongst others. Stability is among the many most important factors worrying if an adaptive neural network controller is to be used in smart functions.
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