Application of neural networks in production system's simulation

Nowadays the most powerful tool to evaluate complex production processes' performance is simulation software. This approach is computationally slow. So one alternative could be an approximation of the original system's model able to detect the relationships between input and output, yet it's computationally more efficient than simulation. During training, a neural network is presented with several input/output pairs, and it learns the functional relationship between input and outputs of the simulation model. The network can guess the output for inputs other than the ones presented during training. Usually it's unknown what are the most significant variables for the output of a system. For this problem, it can be used as a data mining preprocess analysis to see the influence of each input parameter on the performance of the production process. In this paper it is shown a statistical nonparametric method, based on 8Parzen window, to make a data mining analysis and then a multilayer perceptron to approximate the original system.