The quantitative study of conflict management is concerned with finding models
which are accurate and also capable of providing a causal interpretation of results.
This dissertation applies computational intelligence methods to study interstate disputes.
Both multilayer perceptron neural networks and Takagi-Sugeno neuro-fuzzy
models are used to model interstate interactions. The multilayer perceptron neural
network is trained in the Bayesian framework, using the Hybrid Monte Carlo method
to sample from the posterior probabilities. It is found that the network is able to
forecast conflict with an accuracy of 77.3%. A hybrid machine learning method using
the neural network and the genetic algorithm is then presented as a method of
suggesting how conflict can be brought under control. The automatic relevance determination
approach and the sensitivity analysis are used as methods of extracting
causal information from the neural network. The Takagi-Sugeno neuro-fuzzy model
is optimised, using the Gustafson-Kessel clustering algorithm to partion the input
space. It is found that the neuro-fuzzy model predicts conflict with an accuracy of
80.1%. The neuro-fuzzy model is also incorporated into the hybrid machine learning
method to suggest how the identified conflict cases can be avoided. The casual
interpretation is then formulated by a linguistic approximation of the fuzzy rules
extracted from the neuro-fuzzy model. The major finding in this work is that the
interpretations drawn from both the neural network and the neuro-fuzzy model are
consistent.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:wits/oai:wiredspace.wits.ac.za:10539/5863 |
| Date | 03 December 2008 |
| Creators | Tettey, Thando |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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