Identification of Nonlinear Dynamic System Using a Novel Recurrent Wavelet Neural Network Based on the Pipelined Architecture

• Published in: IEEE transactions on industrial electronics (1982) 2014-08, Vol.61 (8), p.4171-4182
• Main Authors: Zhao, Haiquan, Gao, Shibin, He, Zhengyou, Zeng, Xiangping, Jin, Weidong, Li, Tianrui
• Format: Article
• Language: English
• Subjects: Algorithms; Architecture; Artificial neural networks; Computational modeling; Computer architecture; Computer simulation; Dynamical systems; Heuristic algorithms; Modular; Modules; Neural networks; Nonlinear dynamical systems; Nonlinear dynamics; Nonlinear system identification; pipelined recurrent neural network (PRNN); real-time recurrent learning (RTRL); Recurrent neural networks; recurrent wavelet neural network (RWNN); Vectors
• ISSN: 0278-0046; 1557-9948
• DOI: 10.1109/TIE.2013.2288196

This paper presents a novel modular recurrent neural network based on the pipelined architecture (PRWNN) to reduce the computational complexity and improve the performance of the recurrent wavelet neural network (RWNN). The PRWNN inherits the modular architectures of the pipelined recurrent neural network proposed by Haykin and Li and is made up of a number of RWNN modules that are interconnected in a chained form. Since those modules of the PRWNN can be simultaneously performed in a pipelined parallelism fashion, this would lead to a crucial improvement of computational efficiency. Furthermore, owing to the cascade interconnection of dynamic modules, the performance of the PRWNN can be further enhanced. An adaptive gradient algorithm based on the real-time recurrent learning is derived to suit for the modular PRWNN. Simulation examples are given to evaluate the effectiveness of the PRWNN model on the identification of nonlinear dynamic systems and analysis of sunspot number time series. According to simulation results, it is clearly shown that the PRWNN provides impressive better performance in comparison with the single RWNN model.