Grammatical Swarm and Particle Swarm Optimization models applied to Neural Network learning and topology definition

被引：0

作者：

Gomez, Nuria ^{[1
]}

Mingo, Luis F. ^{[1
]}

Garitagoitia, Juan ^{[1
]}

Martinez, Victor ^{[1
]}

Calvo Manzano, Jose A. ^{[2
]}

机构：

[1] Univ Politecn Madrid, Escuela Univ Informat, Crta Valencia Km 7, Madrid 28031, Spain

[2] Univ Politecn Madrid, Fac Informat, E-28660 Madrid, Spain

来源：

PROCEEDINGS OF THE 8TH WSEAS INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE, MAN-MACHINE SYSTEMS AND CYBERNETICS (CIMMACS '09) | 2009年

关键词：

Social Intelligence; Neural Networks; Grammatical Swarm; Particle Swarm Optimization;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

There exists a clear difference between cooperative and competitive strategies. The former ones are based on the swarm colonies, in which all individuals share its knowledge about the goal in order to pass such information to other individuals to get optimum solution. The latter ones are based on genetic models, that is, individuals can die and new individuals are created combining information of alive one; or are based on molecular/celular behaviour passing information from one structure to another. A Grammatical Swarm model is applied to obtain the Neural Network topology of a given problem, training the net with a Particle Swarm algorithm. This paper just shows some ideas in order to obtain an automatic way to define the most suitable neural network topology for a given patter set.

引用

页码：180 / +

页数：2

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