Titlebook: Artificial Neural Networks for Modelling and Control of Non-Linear Systems; Johan A. K. Suykens,Joos P. L. Vandewalle,Bart L. Book 1996 S

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d novel network architectures andlearning algorithms for modelling and control. Topics includenon-linear system identification, neural optimal control, top-downmodel based neural control design and stability analysis of neuralcontrol systems. A major contribution of this book is to introduce.NLq. .Theory. as 978-1-4419-5158-8978-1-4757-2493-6

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Advances in Solid State Physicsction 3.7 simulated and real life examples are presented on nonlinear system identification using feedforward as well as recurrent type of neural networks. New contributions are made in Sections 3.2.2, 3.2.3, 3.3.2, 3.6 and 3.7.

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Johan A. K. Suykens,Joos P. L. Vandewalle,Bart L.

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Introduction,ning modes and some brief history. In Section 1.2 we motivate the use of artificial neural networks for modelling and control. In Section 1.3 we sketch the broad picture of this book, together with a Chapter by Chapter overview. In Section 1.4 own contributions are listed.

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Artificial neural networks: architectures and learning rules,the multilayer perceptron and the radial basis function network. This Chapter is organized as follows. In Section 2.1 we give a description of the architectures. In Section 2.2 we present an overview of universal approximation theorems, together with a brief historical context. In Section 2.3 classi