Least squares wavelet support vector machines for nonlinear system identification. (English) Zbl 1084.68849

Yu, Zhenhua; Cai, Yuanli

Wang, Jun (ed.) et al., Advances in neural networks – ISNN 2005. Second international symposium on neural networks, Chongqing, China, May 30 – June 1, 2005. Proceedings, Part II. Berlin: Springer (ISBN 3-540-25913-9/pbk). Lecture Notes in Computer Science 3497, 436-441 (2005).

Summary: A novel admissible support vector kernel, namely the wavelet kernel satisfying wavelet frames, is presented based on the wavelet theory. The wavelet kernel can approximate arbitrary functions, and is especially suitable for local signal analysis, hence the generalization ability of the support vector machines (SVM) is improved. Based on the wavelet kernel and the least squares support vector machines, the least squares wavelet support vector machines (LS-WSVM) are constructed. In order to validate the performance of the wavelet kernel, LS-WSVM is applied to a nonlinear system identification problem, and the computational process is compared with that of the Gaussian kernel. The results show that the wavelet kernel is more efficient than the Gaussian kernel.
For the entire collection see [Zbl 1073.68014].

MSC:

68T05	Learning and adaptive systems in artificial intelligence
92B20	Neural networks for/in biological studies, artificial life and related topics
93A30	Mathematical modelling of systems (MSC2010)