A hybrid approach for parameter optimization of RBF-AR model

Author

Gan, Min ; Peng, Hui

Author_Institution

Sch. of Inf. Sci. & Eng., Central South Univ., Changsha, China

fYear

2010

fDate

15-17 Dec. 2010

Firstpage

4423

Lastpage

4428

Abstract

A hybrid global-local optimization algorithm for radial basis function (RBF) networks and RBF nets-based state-dependent autoregressive (RBF-AR) models parameter estimation is presented. This algorithm (EA-SNPOM) effectively combines an evolutionary algorithm (EA) with a gradient-based search strategy named the structured nonlinear parameter optimization method (SNPOM). The hybrid approach provides a global search with the EA and a local search via the SNPOM. The effectiveness of the resulting combination is demonstrated by several examples.

Keywords

autoregressive processes; evolutionary computation; gradient methods; radial basis function networks; EA-SNPOM; RBF nets-based state-dependent autoregressive models; RBF-AR model; evolutionary algorithm; gradient-based search strategy; radial basis function; structured nonlinear parameter optimization method; Brain modeling; Computational modeling; Data models; Mathematical model; Optimization; Radial basis function networks; Time series analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control (CDC), 2010 49th IEEE Conference on

Conference_Location

Atlanta, GA

ISSN

0743-1546

Print_ISBN

978-1-4244-7745-6

Type

conf

DOI

10.1109/CDC.2010.5716950

Filename

5716950

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2563808