Adaptive RBF neural network training algorithm for nonlinear and nonstationary signal

Seng Kah Phooi, L. M. Ang

Research output: Book chapter/Published conference paperConference paper

Abstract

This paper presents an improved adaptive radial basis function neural network (RBF NN) for nonlinear and nonstationary signal. The proposed method possesses distinctive properties of Lyapunov Theorybased Adaptive Filtering (LAF) in [1]-[2]. This method is different from many RBF NN training methods using gradient search techniques. A new Lyapunov function of the error between the desired output and the RBF NN output is first defined. The output asymptotically converges to the desired output by proper design of the weight adaptation law in Lyapunov sense. In this paper, we have proved that the design is independent of statistic properties of the input and output signals. The proposed method has better tracking capability compared with the LAF in [1]-[2]. The performance of the proposed technique is illustrated through the nonlinear adaptive prediction of nonstationary speech signals.

Original languageEnglish
Title of host publication2006 International Conference on Computational Intelligence and Security, ICCIAS 2006
Pages433-436
Number of pages4
Volume1
DOIs
Publication statusPublished - 01 Dec 2007
Event2006 International Conference on Computational Intelligence and Security, ICCIAS 2006 - Guangzhou, China
Duration: 03 Oct 200606 Oct 2006

Conference

Conference2006 International Conference on Computational Intelligence and Security, ICCIAS 2006
CountryChina
CityGuangzhou
Period03/10/0606/10/06

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    Phooi, S. K., & Ang, L. M. (2007). Adaptive RBF neural network training algorithm for nonlinear and nonstationary signal. In 2006 International Conference on Computational Intelligence and Security, ICCIAS 2006 (Vol. 1, pp. 433-436). [4072123] https://doi.org/10.1109/ICCIAS.2006.294170