Controlling interstate conflict using neuro-fuzzy modeling and genetic algorithms

Thando Tettey, Tshilidzi Marwala

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Citations (Scopus)

Abstract

The paper introduces neuro-fuzzy modeling to the problem of controlling interstate conflict. It is shown that a neuro-fuzzy model achieves a prediction accuracy similar to Bayesian trained neural networks. It is further illustrated that a neuro-fuzzy model can be used in a Genetic Algorithm (GA) based control scheme to avoid 100% of the detected conflict cases. The neuro-fuzzy model is then suggested as a more suitable option to neural networks as the model offers information transparency in the form of fuzzy rules, as compared to the weights of the neural network.

Original languageEnglish
Title of host publicationINES 2006
Subtitle of host publication10th International Conference on Intelligent Engineering Systems 2006
Pages30-34
Number of pages5
Publication statusPublished - 2006
Externally publishedYes
EventINES 2006: 10th International Conference on Intelligent Engineering Systems 2006 - London, United Kingdom
Duration: 26 Jun 200628 Jun 2006

Publication series

NameINES 2006: 10th International Conference on Intelligent Engineering Systems 2006

Conference

ConferenceINES 2006: 10th International Conference on Intelligent Engineering Systems 2006
Country/TerritoryUnited Kingdom
CityLondon
Period26/06/0628/06/06

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Applied Mathematics
  • Theoretical Computer Science

Fingerprint

Dive into the research topics of 'Controlling interstate conflict using neuro-fuzzy modeling and genetic algorithms'. Together they form a unique fingerprint.

Cite this