Controlling interstate conflict using neuro-fuzzy modeling and genetic algorithms

Thando Tettey; Tshilidzi Marwala

Controlling interstate conflict using neuro-fuzzy modeling and genetic algorithms

Thando Tettey, Tshilidzi Marwala

University of the Witwatersrand

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

15 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 language	English
Title of host publication	INES 2006
Subtitle of host publication	10th International Conference on Intelligent Engineering Systems 2006
Pages	30-34
Number of pages	5
Publication status	Published - 2006
Externally published	Yes
Event	INES 2006: 10th International Conference on Intelligent Engineering Systems 2006 - London, United Kingdom Duration: 26 Jun 2006 → 28 Jun 2006

Publication series

Name	INES 2006: 10th International Conference on Intelligent Engineering Systems 2006

Conference

Conference	INES 2006: 10th International Conference on Intelligent Engineering Systems 2006
Country/Territory	United Kingdom
City	London
Period	26/06/06 → 28/06/06

ASJC Scopus subject areas

Artificial Intelligence
Software
Applied Mathematics
Theoretical Computer Science

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

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title = "Controlling interstate conflict using neuro-fuzzy modeling and genetic algorithms",

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.",

author = "Thando Tettey and Tshilidzi Marwala",

year = "2006",

language = "English",

isbn = "0780397088",

series = "INES 2006: 10th International Conference on Intelligent Engineering Systems 2006",

pages = "30--34",

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Tettey, T & Marwala, T 2006, Controlling interstate conflict using neuro-fuzzy modeling and genetic algorithms. in INES 2006: 10th International Conference on Intelligent Engineering Systems 2006., 1689336, INES 2006: 10th International Conference on Intelligent Engineering Systems 2006, pp. 30-34, INES 2006: 10th International Conference on Intelligent Engineering Systems 2006, London, United Kingdom, 26/06/06.

Controlling interstate conflict using neuro-fuzzy modeling and genetic algorithms. / Tettey, Thando; Marwala, Tshilidzi.
INES 2006: 10th International Conference on Intelligent Engineering Systems 2006. 2006. p. 30-34 1689336 (INES 2006: 10th International Conference on Intelligent Engineering Systems 2006).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Controlling interstate conflict using neuro-fuzzy modeling and genetic algorithms

Abstract

Publication series

Conference

ASJC Scopus subject areas

UN SDGs

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