Identification of singularly perturbed nonlinear system using recurrent high-order neural network

Abstract

In this paper, a new discrete time identification scheme for a singularly perturbed nonlinear system using recurrent high order multi-time scale neural network is presented. The high-order neural network (HONN) is known for its simple structure and powerful nonlinearity approximation property, which make it more suitable for modeling the singularly perturbed nonlinear systems than the multi-layer neural network [10]. An on-line identification scheme-optimal bounded ellipsoid (OBE) algorithm is developed for the recurrent high order neural network (RHONN) model. By adaptively changing the learning rate, the on-line identification scheme can achieve faster convergence compared to the other widely used learning schemes, such as backpropagation. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.Index Terms -Recurrent high order neural network, optimal bounded ellipsoid, multi time-scale system.