Template based black-box optimization of dynamic neural fields

被引：6
|
作者：
Fix, Jeremy [1 ]
机构：
[1] UMI 2958 Georgia Tech CNRS, IMS, Supelec, F-57070 Metz, France
来源：
NEURAL NETWORKS | 2013年 / 46卷
关键词：
Dynamic neural fields; Optimization; Particle swarm optimization; Covariance Matrix Adaptation Evolution Strategy; CMA EVOLUTION STRATEGY; SELF-ORGANIZATION; SIGNALS; MODEL;
D O I：
10.1016/j.neunet.2013.04.008
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Due to their strong non-linear behavior, optimizing the parameters of dynamic neural fields is particularly challenging and often relies on expert knowledge and trial and error. In this paper, we study the ability of particle swarm optimization (PSO) and covariance matrix adaptation (CMA-ES) to solve this problem when scenarios specifying the input feeding the field and desired output profiles are provided. A set of spatial lower and upper bounds, called templates are introduced to define a set of desired output profiles. The usefulness of the method is illustrated on three classical scenarios of dynamic neural fields: competition, working memory and tracking. (C) 2013 Elsevier Ltd. All rights reserved.
引用
收藏
页码：40 / 49
页数：10
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