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Title: Optimization of global model composed of radial basis functions using the term-ranking approach

Abstract

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.

Authors:
; ;  [1]
  1. Key Laboratory of Modern Acoustics, Nanjing University, Nanjing 210093 (China)
Publication Date:
OSTI Identifier:
22251015
Resource Type:
Journal Article
Journal Name:
Chaos (Woodbury, N. Y.)
Additional Journal Information:
Journal Volume: 24; Journal Issue: 1; Other Information: (c) 2014 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 1054-1500
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; CAPTURE; CHAOS THEORY; COMPUTERIZED SIMULATION; FUNCTIONS; OPTIMIZATION; PERIODICITY; SIGNALS

Citation Formats

Cai, Peng, Tao, Chao, and Liu, Xiao-Jun. Optimization of global model composed of radial basis functions using the term-ranking approach. United States: N. p., 2014. Web. doi:10.1063/1.4869349.
Cai, Peng, Tao, Chao, & Liu, Xiao-Jun. Optimization of global model composed of radial basis functions using the term-ranking approach. United States. https://doi.org/10.1063/1.4869349
Cai, Peng, Tao, Chao, and Liu, Xiao-Jun. 2014. "Optimization of global model composed of radial basis functions using the term-ranking approach". United States. https://doi.org/10.1063/1.4869349.
@article{osti_22251015,
title = {Optimization of global model composed of radial basis functions using the term-ranking approach},
author = {Cai, Peng and Tao, Chao and Liu, Xiao-Jun},
abstractNote = {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.},
doi = {10.1063/1.4869349},
url = {https://www.osti.gov/biblio/22251015}, journal = {Chaos (Woodbury, N. Y.)},
issn = {1054-1500},
number = 1,
volume = 24,
place = {United States},
year = {Sat Mar 15 00:00:00 EDT 2014},
month = {Sat Mar 15 00:00:00 EDT 2014}
}