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

18 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

UN SDGs

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

SDG 16 Peace, Justice and Strong Institutions

ASJC Scopus subject areas

Artificial Intelligence
Software
Applied Mathematics
Theoretical Computer Science

Cite this

@inproceedings{0f9eea00cc1b4411a2b41df479fd36c8,

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",

booktitle = "INES 2006",

note = "INES 2006: 10th International Conference on Intelligent Engineering Systems 2006 ; Conference date: 26-06-2006 Through 28-06-2006",

}

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

TY - GEN

T1 - Controlling interstate conflict using neuro-fuzzy modeling and genetic algorithms

AU - Tettey, Thando

AU - Marwala, Tshilidzi

PY - 2006

Y1 - 2006

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/34547257882

M3 - Conference contribution

AN - SCOPUS:34547257882

SN - 0780397088

SN - 9780780397088

T3 - INES 2006: 10th International Conference on Intelligent Engineering Systems 2006

SP - 30

EP - 34

BT - INES 2006

T2 - INES 2006: 10th International Conference on Intelligent Engineering Systems 2006

Y2 - 26 June 2006 through 28 June 2006

ER -

Controlling interstate conflict using neuro-fuzzy modeling and genetic algorithms

Abstract

Publication series

Conference

UN SDGs

ASJC Scopus subject areas

Other files and links

Fingerprint

Cite this