A novel time-frequency analysis approach using multiscale radial basis functions aided by a modified particle swarm optimization(PSO) algorithm

LIU Qing; LI Yang; TAN Sirui

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A novel time-frequency analysis approach using multiscale radial basis functions aided by a modified particle swarm optimization(PSO) algorithm

LIU Qing #,LI Yang *,TAN Sirui

School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191

*Correspondence author

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Funding: National Natural Science Foundation of China （No.No. 61403016）, Specialized Research Fund for the Doctoral Program of Higher Education （No.No. 20131102120008）

Opened online: 7 April 2015

Accepted by: none

Citation: LIU Qing,LI Yang,TAN Sirui.A novel time-frequency analysis approach using multiscale radial basis functions aided by a modified particle swarm optimization(PSO) algorithm[OL]. [ 7 April 2015] http://en.paper.edu.cn/en_releasepaper/content/4637119

An efficient time-varying autoregressive (TVAR) modeling approach using the multiscale radial basis functions (MRBF) method is presented for analyzing nonstationary signal processing, with application to artificial EEG data modeling and power spectral estimation. In the new parametric modeling framework, the time-dependent coefficients in the TVAR model are approximated by using MRBF that can better identify time-varying parameters with a variety of dynamic processes in nonstationary signals. Thus, the time-varying modeling problem is simplified to optimal scale determination of MRBF and parameter estimation, which can be effectively resolved by a modified particle swarm optimization (PSO) method and an ordinary least square (OLS) algorithm, respectively. To evaluate the effectiveness of the proposed approach, a comparison with recursive least squares (RLS) and Legendre polynomials expansion method for a synthesized EEG signal is performed. Results demonstrate the proposed approach can indeed provide optimal time-frequency resolution as compared to RLS and Legendre polynomials expansion.

Keywords:System identification; Legendre polynomials; Multiscale radial basis functions; Modified PSO algorithm; Time-varying models; Time-frequency spectra.

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