Optimization of ANN Architecture and Training Parameters Using Taguchi Method

Abstract

Artificial neural networks (ANNs) have gained significant popularity for modeling and optimization. Choosing appropriate training and design parameters for an ANN, on the other hand, is still a difficult task. These parameters are typically chosen by a trial-and-error strategy in which a large number of ANN models are developed and compared. This evidence stated how the Taguchi approach can be used to enhance an ANN model. To show the technique, a case study of a minimization of Green Sand-casting defects in a green sand-casting process is provided. Total 175 samples were taken for the study. L18 orthogonal array was used to construct the ANN training and architectural parameters. A Taguchi-optimized ANN model was created and showed excellent prediction accuracy. Analyses and experiments have shown that the best ANN training and architectural parameters may be found in a systematic manner, minimizing the time-consuming trial-and-error method.