A Cooperative Recurrent Neural Network Algorithm for Parameter Estimation of Autoregressive Signals

Abstract

A cooperative recurrent neural network (CRNN) algorithm for parameter estimation of autoregressive (AR) signals is proposed in this paper. The proposed CRNN algorithm is based on a generalized least absolute deviation (GLAD) method, which generalizes significantly the conventional least absolute deviation method. Compared with second-order and high-order statistic algorithms, the proposed CRNN algorithm can obtain robustly an optimal AR parameter estimation without requiring measurement Gaussian noise. Unlike existing cooperative neural network algorithms, the proposed CRNN algorithm has a global convergence and a novel weighting cooperation scheme to integrate single neural network output automatically. Simulation results shows that the more accurate estimates can be attained by the proposed CRNN algorithm in the presence of non-Gaussian colored noise.