Novel FTLR NN Model with Gamma Memory Filter for Identification of a Typical Magnetic Stirrer

In this paper, a novel focused time lagged recurrent neural network (FTLR NN) with gamma memory filter is designed to learn the subtle complex dynamics of a typical magnetic stirrer process. Magnetic stirrer exhibits complex nonlinear operations where reaction is exothermic. It appears to us that identification of such a highly nonlinear system is not yet reported by other researchers using neural networks. As magnetic stirrer process includes time relationship in the input-output mappings, time lagged recurrent neural network is particularly used for identification purpose. The standard back propagation algorithm with momentum term has been proposed in this model. The various parameters like number of processing elements, number of hidden layers, training and testing percentage, learning rule and transfer function in hidden and output layer are investigated on the basis of performance measures like MSE, NMSE and correlation coefficient on testing data set. Finally, effect of different norms are tested along with variation in gamma memory filter. It is shown that dynamic NN model has a remarkable system identification capability for the problem considered in this paper. Thus, FTLR NN with gamma memory filter can be used to learn underlying highly nonlinear dynamics of the system, which is major contribution of this paper.