Optimizing Neural Network Parameters Using Taguchi’s Design of Experiments Approach: An Application for Equivalent Stress Prediction Model of Automobile Chassis

Artificial neural networks (ANNs) have been successfully applied to problems in different fields including medicine, management, and manufacturing. One major disadvantage of ANNs is that there is no systematic approach for model design. Most literature suggests a trial-and-error method for parameter setting which requires more time. The accuracy of the ANN model greatly depends on the network parameter settings including the number of neurons, momentum, learning rate, transfer function, and training algorithm. In this paper, we apply Taguchi’s design of experiments approach to determine the optimum set of parameters for an ANN trained using feed forward back-propagation. We present a case study of an equivalent stress prediction model for an automobile chassis to demonstrate the implementation of the approach. After training the network, the optimum values of the ANN parameters are determined according to the performance statistics. The performance of the ANN is superior using the Taguchi method to optimize the parameters compared with random parameter values.