Application of Neural Networks to Modelling and ControlG. F. Page, J. B. Gomm, D. Williams |
Contents
Introduction to neural networks | 1 |
evolution revolution or renaissance | 9 |
Identification of linear systems using recurrent neural networks | 25 |
Copyright | |
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achieved adaptive control Adaptive network algorithm anaesthesia anaesthetic ANNAD application approach artificial neural networks back-propagation basis functions Bias CMAC coefficients Control Systems described dynamic systems Engineering estimations for phase FANN fermentation process gradient descent hidden layer Hidden node hidden units Hopfield network identification initial weights input and output input layer Input node Isoflurane iterations Kalman filter Kohonen network learning rate linear methods modelling and control modified Elman MTSSE network output network topology network training neural nets neural network model Newcastle upon Tyne non-linear process number of neurons Number of patterns on-line output layer output nodes parameters performance polynomial polynomial order predictive control principal component principal component analysis problem Proc procedure processing elements product concentration r.m.s. error real poles reference model RESAC NN Sample number Fig seed(u sigmoidal simulations SNDM network spread encoding step structure technique testing third-order system training set tuning module values variables zero