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 -. 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 -. The performance of the proposed technique is illustrated through the nonlinear adaptive prediction of nonstationary speech signals.
|Title of host publication||2006 International Conference on Computational Intelligence and Security, ICCIAS 2006|
|Number of pages||4|
|Publication status||Published - 01 Dec 2007|
|Event||2006 International Conference on Computational Intelligence and Security, ICCIAS 2006 - Guangzhou, China|
Duration: 03 Oct 2006 → 06 Oct 2006
|Conference||2006 International Conference on Computational Intelligence and Security, ICCIAS 2006|
|Period||03/10/06 → 06/10/06|