Global optimization of radial basis function networks by hybrid simulated annealing

Title:

Global optimization of radial basis function networks by hybrid simulated annealing

Creator:

Lee, Jong-Seok and Park, Cheol Hoon

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:fac82f79-9839-47a5-b904-b841331c82f3
uuid:fac82f79-9839-47a5-b904-b841331c82f3

Subject:

Radial basis function network, hybrid simulated annealing, and global optimization

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

This paper presents a global optimization method of radial basis function networks. In the proposed method, stochastic search by simulated annealing is combined with a local search technique in order to perform global optimization of the network parameters with enhanced convergence speed. Its convergence property is proved mathematically. Experimental results demonstrate that the proposed method improves the performance of the networks over the conventional local and global training methods and reduces influence of the initial parameter values on the final results.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2010 Volume:20 | Number:4

Harvested from:

CDK

Metadata only:

false