Evolving RBF networks in a multi-agent system

Title:

Evolving RBF networks in a multi-agent system

Creator:

Byrski, Aleksander and Kisiel-Dorohinicki, Marek

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:60ac7bb3-fc53-45a1-99c4-2c036e4f852a
uuid:60ac7bb3-fc53-45a1-99c4-2c036e4f852a

Subject:

multi agent systems, artificial neural networks, radial basis function networks, and predictive modular neural networks

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

In the paper an agent system of evolving neural networks, being an example of collective intelligence, is presented. A concept of decentralised evolutionary computation realised as an evolutionary multi-agent systém (EMAS) may help to avoid some of the shortcomings of classical evolutionary optimisation techniques. As an addition, several methods of managing such a collective intelligent systém are mentioned. General considerations are illustrated by a particular system dedicated to the problém of time-series prediction. Selected experimental results conclude the work.

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 | 2002 Volume:12 | Number:5

Harvested from:

CDK

Metadata only:

false