This paper proposes a new soft computing approach to artificial neural network (ANN) model identification. The new approach is based upon big bang big crunch (BB-BC) optimization algorithm .To test our approach we have identified two models one from control field namely rapid battery charger and second a rating system for institutes of higher learning. With about 20% of the total data being used for training the proposed approach was able to identify models successfully. In order to validate our proposed approach, we implemented the approach in the MATLAB and compared its training performance with 6 other well known classical training approaches namely Levenberg-Marquardt algorithm (LM), error back propagation(EBP), Resilent prop(RPROP), particle swarm optimization (PSO), ant colony optimization(ACO) and artificial bee colony(ABC). It was observed that BB-BC has faster convergence speed and produced better results than the other approaches.

Model Identification, ANN (Artificial Neural Network), Optimization

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