Prediction Model using a Multi-Layer Perceptron Neural Network for Military Plastic Explosive PE4 Blast Performance in a Significant Effect of Tropicalisation

Over the years, plastic explosive PE4 has been imported from the United Kingdom and is fully utilised in military activities and drills. This study is to discover a ratio of the plastic explosive PE4 explosion performance in tropical conditions and develop a multilayer perceptron neural network model for the prediction of plastic explosive in Malaysia. Several environmental tests have been performed in Kem Kongkoi, Jelebu, Negeri Sembilan. Six parameters were considered, including environmental temperature, distance measurement, explosive material, variety of shapes, weight and ignition point. In this paper, the Bayesian Regularization, Levenberg-Marquardt algorithm and Scaled Conjugate Gradient model were developed. Every model was tested with 24 datasets to discover the root mean square error and regression performance. The Bayesian Regularization model provides the best prediction model as it has a mean square error of 0.0005 and a regression performance value that is close to 1 at 0.9992.

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