The ability of a neural network to realize some complex nonlinear function makes them attractive for system identification. In the recent past, neural networks trained with back-propagation (BP) learning algorithm have gained attention for the identification of nonlinear dynamic systems. Slower convergence and longer training times are the disadvantages often mentioned when the standard BP algorithm are compared with other competing techniques. In addition, in the standard BP algorithm, the learning rate is fixed and that it is uniform for all weights in a layer. In this paper, we present an improvement to the standard BP algorithm based on the use of an adaptive learning rate and momentum term, where the learning rate is adjusted at each iteration to reduce the training time. Simulation results indicate a faster convergence speed and better error minimization as compared to other competing methods.
This paper demonstrates that neural networks can be used effectively for control of nonlinear dynamical systems. The proposed control scheme is based on the artificial neural network and is applied to an isothermic continuous stirred tank reactor (CSTR). In this paper we have tested the internal model control (IMC) strategy based on neural networks for process systems. This approach of control uses two Feed Forward Neural networks (FFNN), one as an identifier and the other as a controller. Multilayer neural network has been used for forward modeling and the inverse model of the process which has been determined off line using input output data of process, as controller. The modified back propagation algorithm has been used to train the neural networks. Neural network based IMC scheme has been implemented for both set point and regulatory control action and the comparison have been made for a set of constant momentum term.
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