Optimal design of neural networks using the Taguchi method

Abstract In the last five years, many new learning algorithms have been designed and developed to train neural networks for solving complex problems in a wide variety of domains. One of the principal deficiencies with current neural network research is associated with the design of the neural networks. The design of a neural network involves the selection of an optimal set of design parameters to achieve fast convergence speed during training and the required accuracy during recall. These design parameters include both the micro-structural and macro-structural aspects of a neural network. This paper describes an innovative application of the Taguchi method for the determination of these parameters to meet the training speed and accuracy requirements. Using the Taguchi method, both the micro-structural and macro-structural aspects of the neural network design parameters can be considered concurrently. The feasibility of using this approach is demonstrated in this paper by optimizing the design parameters of a back-propagation neural network for determining operational policies for a manufacturing system. Results drawn from this research show that the Taguchi method provides an effective means to enhance the performance of the neural network in terms of the speed for learning and the accuracy for recall.