Recurrent context layered radial basis function neural network for the identification of nonlinear dynamical systems

Abstract

This paper proposes a novel recurrent context layered radial basis function neural network (RCLRBFNN) for the identification of nonlinear dynamical systems. The proposed model consists of an additional context layer in which the nodes represent the unit-delayed outputs of the hidden layer radial centers. These delayed outputs undergo a nonlinear transformation by applying a tangent hyperbolic function. These transformed signals connect to the output layer neuron through the adjustable context layered weights. To tune the parameters of the proposed model the update equations are derived using the dynamic back-propagation algorithm. Further, an adaptive learning rate scheme is proposed to improve the performance of the learning algorithm. In the simulation experiment, a total of two examples are considered to test the efficacy and performance of the proposed model. The performance comparison is made with the conventional structure of the radial basis function neural network (RBFNN), Jordan Recurrent neural network (JRNN), and the feed-forward neural network (FFNN) (which is nothing but a single-layered multi-layered perceptron). Both the disturbance signal as well as system’s uncertainty scenarios are considered to test the robustness shown by the proposed model. The results showed that the proposed model has delivered a better identification accuracy as compared to the other neural models.