A neural network approach to the validation of simulation models

We tackle the problem of validating simulation models using neural networks. We propose a neural-network-based method that first learns key properties of the behaviour of alternative simulation models, and then classifies real system behaviour as coming from one of the models. We investigate the use of multi-layer perceptron and radial basis function networks, both of which are popular pattern classification techniques. By a computational experiment, we show that our method successfully allows to distinguish valid from invalid models for a multiserver queueing system.status: publishe

A NEURAL NETWORK APPROACH TO THE VALIDATION OF SIMULATION MODELS

We tackle the problem of validating simulation models using neural networks. We propose a neural-network-based method that first learns key properties of the behaviour of alternative simulation models, and then classifies real system behaviour as coming from one of the models. We investigate the use of multi-layer perceptron and radial basis function networks, both of which are popular pattern classification techniques. By a computational experiment, we show that our method successfully allows to distinguish valid from invalid models for a multiserver queueing system