Controlling Interstate Conflict using Neuro-fuzzy Modeling and Genetic Algorithms

Author

Tettey, Thando ; Marwala, Tshilidzi

Author_Institution

Sch. of Electr. & Inf. Eng., Univ. of the Witwatersrand, Johannesburg

fYear

0

fDate

0-0 0

Firstpage

30

Lastpage

34

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

Keywords

fuzzy control; fuzzy logic; fuzzy neural nets; fuzzy reasoning; fuzzy set theory; fuzzy systems; genetic algorithms; politics; Bayesian trained neural network; fuzzy rules; genetic algorithm based control scheme; interstate conflict control problem; neuro-fuzzy modeling; Africa; Artificial neural networks; Fuzzy logic; Fuzzy systems; Genetic algorithms; Genetic engineering; Humans; Neural networks; Predictive models; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Engineering Systems, 2006. INES '06. Proceedings. International Conference on

Conference_Location

London

Print_ISBN

0-7803-9708-8

Type

conf

DOI

10.1109/INES.2006.1689336

Filename

1689336