TY - GEN
T1 - A novel time-frequency analysis in nonstationary signals based multiscale radial basis functions and forward orthogonal regression
AU - Xudong, Wang
AU - Lina, Wang
AU - Yang, Li
N1 - Publisher Copyright:
© Springer Nature Singapore Pte Ltd. 2017.
PY - 2017
Y1 - 2017
N2 - For time-frequency analysis of nonstationary signals, an adaptive and efficient time-varying autoregressive (TVAR) modeling method based on the multiscale radial basis function (MRBF) network and forward orthogonal regression (FOR) algorithm is investigated in this paper. Specifically, time-varying coefficients in the TVAR model is firstly approximated by the MRBF which has a better performance of tracking the time-varying parameters in nonstationary signals. Thus, the time-varying modeling problem is simplified to the selection of optimal centers and scales of MRBF, which a modified particle swarm optimization (MPSO) method aided by a FOR algorithm are resolved. Secondly, recursive least squares (RLS), Legendre polynomials expansion method and single scale radial basis function approach (SSRBF) are used to compare with the proposed method to evaluate the performance. Finally, the experimental results indicate that the proposed approach outperforms competing techniques in terms of mean absolute error and root mean squared error, and show the effectiveness of the proposed method for extracting the nonstationary signals. abstract environment.
AB - For time-frequency analysis of nonstationary signals, an adaptive and efficient time-varying autoregressive (TVAR) modeling method based on the multiscale radial basis function (MRBF) network and forward orthogonal regression (FOR) algorithm is investigated in this paper. Specifically, time-varying coefficients in the TVAR model is firstly approximated by the MRBF which has a better performance of tracking the time-varying parameters in nonstationary signals. Thus, the time-varying modeling problem is simplified to the selection of optimal centers and scales of MRBF, which a modified particle swarm optimization (MPSO) method aided by a FOR algorithm are resolved. Secondly, recursive least squares (RLS), Legendre polynomials expansion method and single scale radial basis function approach (SSRBF) are used to compare with the proposed method to evaluate the performance. Finally, the experimental results indicate that the proposed approach outperforms competing techniques in terms of mean absolute error and root mean squared error, and show the effectiveness of the proposed method for extracting the nonstationary signals. abstract environment.
KW - Forward orthogonal regression (FOR)
KW - Modified Particle swarm optimization (MPSO)
KW - Multiscale radial basis functions (MRBF)
KW - Time-frequency analysis
KW - Time-varying autoregressive (TVAR)
UR - https://www.scopus.com/pages/publications/85026724793
U2 - 10.1007/978-981-10-5230-9_26
DO - 10.1007/978-981-10-5230-9_26
M3 - 会议稿件
AN - SCOPUS:85026724793
SN - 9789811052293
T3 - Communications in Computer and Information Science
SP - 235
EP - 244
BT - Cognitive Systems and Signal Processing - 3rd International Conference, ICCSIP 2016, Revised Selected Papers
A2 - Sun, Fuchun
A2 - Liu, Huaping
A2 - Hu, Dewen
PB - Springer Verlag
T2 - 3rd International Conference on Cognitive Systems and Information Processing, ICCSIP 2016
Y2 - 19 November 2016 through 23 November 2016
ER -