Using self-organising feature maps for the control of artificial organisms
- 1 January 1993
- journal article
- Published by Institution of Engineering and Technology (IET) in IEE Proceedings D Control Theory and Applications
- Vol. 140 (3) , 176-180
- https://doi.org/10.1049/ip-d.1993.0025
Abstract
Variations on the standard Kohonen feature map can enable an ordering of the map state space by using only a limited subset of the complete input vector. Also it is possible to employ merely a local adaptation procedure to order the map, rather than having to rely on global variables and objectives. Such variations have been included as part of a hybrid learning system (HLS) which has arisen out of a genetic-based classifier system. In this paper a description of the modified feature map is given, which constitutes the HLSs long term memory, and results on the control of a simple maze running task are presented, thereby demonstrating the value of goal related feedback within the overall network.Keywords
This publication has 1 reference indexed in Scilit:
- Self-Organizing Neural Network for Non-Parametric Regression AnalysisPublished by Springer Nature ,1990