An efficient learning algorithm generating small RBF neural networks

Title:

An efficient learning algorithm generating small RBF neural networks

Creator:

Lai, Xiaoping and Li, Bin

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:7c552851-fe88-4d6f-8865-4987b2cfd2d7
uuid:7c552851-fe88-4d6f-8865-4987b2cfd2d7

Subject:

Radial basis function neural network, growth criterion, orthogonalized least square, and pruning strategy

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

This paper presents an efficient learning algorithm that generates radial basis function neural network with few neurons. The neural network adds neurons according to a growth criterion defined by the current output error, the current input's distance to the nearest center, and the root-mean-square output error over a sliding windows, deletes neurons by a pruning strategy based on the error reduction rates, and updates the output-layer weights with a Givens QR decomposition based on the orthogonalized least square algorithm. Simulations on two benchmark problems demonstrate that the algorithm produces smaller networks than RAN, RANEKF, and MRAN, and consumes less training time than RAN, RANEKF, MRAN, and GAP-RBF.

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 | 2005 Volume:15 | Number:6

Harvested from:

CDK

Metadata only:

false