An evolutionary RBF network configuration using adaptive width adjustment based on vaccination

Title:

An evolutionary RBF network configuration using adaptive width adjustment based on vaccination

Creator:

Zang, Xiaogang, Gong, Xinbao, Ling, Xiaofeng, Chang, Cheng , and Tang , Bin

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:b52c77fc-56be-4fd7-963e-9175a3b848b3
uuid:b52c77fc-56be-4fd7-963e-9175a3b848b3

Subject:

Computational learning theory, evolutionary computation, neural networks, RBF networks, and vaccination operation

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

In this paper, a mechanism of adaptive width adjustment based on immunological vaccination is proposed for the evolutionary training of RBF neural networks. Inspired by the vaccination process of the natural immune system, the algorithm implements an individual-orientated adaptation of the width in training stages to optimize the potential solutions, therefore reinforces the evolutionary capability and efficiency. A two-layer genotype-coding scheme, which enables a simultaneous evolution of network structure and parameters, is presented to achieve a compact and consistent-in-form solution. The proposed learning strategy is tested on several benchmark problems and results demonstrate promise.

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 | 2008 Volume:18 | Number:4

Harvested from:

CDK

Metadata only:

false