Optimization of global model composed of radial basis functions using the term-ranking approach.
A term-ranking method is put forward to optimize the global model composed of radial basis functions to improve the predictability of the model. The effectiveness of the proposed method is examined by numerical simulation and experimental data. Numerical simulations indicate that this method can significantly lengthen the prediction time and decrease the Bayesian information criterion of the model. The application to real voice signal shows that the optimized global model can capture more predictable component in chaos-like voice data and simultaneously reduce the predictable component (periodic pitch) in the residual signal.
Key Laboratory of Modern Acoustics, Nanjing University, Nanjing 210093, China.
Peng
P
Cai
Key Laboratory of Modern Acoustics, Nanjing University, Nanjing 210093, China.
Chao
C
Tao
Key Laboratory of Modern Acoustics, Nanjing University, Nanjing 210093, China.
Xiao-Jun
XJ
Liu
D006801:Humans; D008962:Models, Theoretical; D014831:Voice