Neuro-Rough Sets for Modeling Interstate Conflict
This chapter investigated a neuro-rough model –a combination of a Multi-Layered Perceptron (MLP) neural network with rough set theory– for the modeling of interstate conflict. The model was formulated using a Bayesian framework and trained using a Monte Carlo technique with the Metropolis criterion. The model was then tested on militarized interstate dispute and was found to combine the accuracy of the Bayesian MLP model with the transparency of the rough set model. The technique presented was compared to the genetic algorithm optimized rough sets. The presented Bayesian neuro-rough model performed better than the genetic algorithm optimized rough set model.
The present study develops a non-parametric Bayesian network (NPBN) model to predict the corrosion depth on buried pipelines using the pipeline age and local soil properties. The dependence structu...