Optimum design for artificial neural networks: an example in a bicycle derailleur system

Abstract

The integration of neural networks and optimization provides a tool for designing network parameters and improving network performance. In this paper, the Taguchi method and the Design of Experiment (DOE) methodology are used to optimize network parameters. The users have to recognize the application problems and choose a suitable Artificial Neural Network model. Optimization problems can then be defined according to the model. The Taguchi method is first applied to a problem to find out the more important factors, then the DOE methodology is used for further analysis and forecasting. A Learning Vector Quantization example is shown for an application to bicycle derailleur systems. (C) 2000 Elsevier Science Ltd. All rights reserved.