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Multi-layer Perceptron and Radial Basis Function for Modeling Interstate Conflict

  • Tshilidzi Marwala
  • Monica Lagazio
Chapter
Part of the Advanced Information and Knowledge Processing book series (AI&KP)

Abstract

This chapter introduces and then compares the multi-layer perceptron neural network to the radial basis function neural network to help understand and predict interstate conflict. These two techniques are described in detail and justified with a review of relevant literature and they are implemented to interstate conflict. The results obtained from the implementation of these techniques demonstrate that the multi-layer perceptron neural network is better at predicting interstate conflict than the radial basis function network. This is mainly due to the cross-coupled chartacteristics of the multi-layer perceptron’s network compared to the radial basis function network.

Keywords

Radial Basis Function Receiver Operating Characteristic Curve Hide Node Hide Neuron Weight Parameter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.University of JohannesburgJohannesburgSouth Africa

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