Nonlinear noise reduction of chaotic time series based on multidimensional recurrent LS-SVM ?
In order to resolve the noise reduction in chaotic time series, a novel method based on multidimensional recurrent least squares support vector machine (MDRLS-SVM) is proposed in this paper. Considering the evolvement feature of the chaotic system, we utilize the recurrent version of least squares support vector machines (LS-SVM) to manipulate the iterative problem. From the high-dimensional phase space point of view, the function approximation in the high-dimensional embedding phase space is carried out and the noise reduction achieved simultaneously based on the reconstructed embedding phase theory. We show by means of simulation of Ikeda map that the proposed method is able to provide accurate results in noise reduction of chaotic system.
noise reduction chaotic time series support vector machine