A proposed evolutionary, self-organizing automaton for the control of dynamic systems

David W. Russell

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

BOXES is a well known methodology that learns to perform control maneuvers for dynamic systems with only cursory a priori knowledge of the mathematics of the system model. A limiting factor in the BOXES algorithm has always been the assignment of appropriate boundaries to subdivide each state variable into regions. In addition to suggesting a method of alleviating this weakness, the paper shows that the accumulated statistical data in near neighboring states may be a powerful agent in accelerating learning, and may eventually provide a possible evolution to self-organization. A heuristic process is postulated that may allow strong areas of knowledge in the system domain to create larger cellular structures, while causing the more delicate, uncertain, or untested regions to take on a finer granularity. The paper theorizes that these untrainable states may contain valuable and sufficient information about the location of switching surfaces. The paper concludes with some future research questions.

Original languageEnglish (US)
Title of host publicationRough Sets and Current Trends in Computing - 3rd International Conference, RSCTC 2002, Proceedings
EditorsJames J. Alpigini, James F. Peters, Andrzej Skowron, Ning Zhong
PublisherSpringer Verlag
Pages33-43
Number of pages11
ISBN (Print)9783540442745
DOIs
StatePublished - 2002
Event3rd International Conference on Rough Sets and Current Trends in Computing, RSCTC 2002 - Malvern, United States
Duration: Oct 14 2002Oct 16 2002

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2475
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other3rd International Conference on Rough Sets and Current Trends in Computing, RSCTC 2002
Country/TerritoryUnited States
CityMalvern
Period10/14/0210/16/02

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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