A Lyapunov-stability-based context-layered recurrent pi-sigma neural network for the identification of nonlinear systems

Abstract

A novel higher-order context-layered recurrent pi-sigma neural network (CLRPSNN) is presented for the identification of nonlinear dynamical systems. The proposed model is the modified form of the classical pi-sigma neural network (PSNN) and contains an additional layer (known as the context layer) of the context nodes. Pi-sigma networks involve a product operator/unit in their output layer which indirectly incorporates in them the capability of higher-order networks and also reduces their network complexity. For tuning the weights of the proposed CLRPSNN model, a learning procedure is developed by combining the Back-Propagation (BP) and Lyapunov-stability method. The performance of the proposed model is compared with other models such as PSNN, Feed-forward neural network (FFNN) (containing single hidden layer), and various popular recurrent neural network (RNN) like Elman recurrent neural network (ERNN), Jordan recurrent neural network (JRNN), Diagonal recurrent neural network (DRNN), and a deep neural network (DNN). The simulation study showed that the proposed model has given better results as compared to the other models.