Title :
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
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;
Conference_Titel :
Decision and Control (CDC), 2010 49th IEEE Conference on
Conference_Location :
Atlanta, GA
Print_ISBN :
978-1-4244-7745-6
DOI :
10.1109/CDC.2010.5716950
