基于多尺度径向基函数和改进粒子群优化算法的时频分析方法

n 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.