Employ the Taguchi Method to Optimize BPNN’s Architectures in Car Body Design System

Previous research works tried to optimize the architectures of Back Propagation Neural Networks (BPNN) in order to enhance their performance. However, the using of appropriate method to perform this task still needs expanding knowledge. The paper studies the effect and the benefit of using Taguchi method to optimize the architecture of BPNN car body design system. The paper started with literatures review to define factors and level of BPNN parameters for number of hidden layer, number of neurons, learning algorithm, and etc. Then the BPNN architecture is optimized by Taguchi method with Mean Square Error (MSE) indicator. The Signal to Noise (S/N) ratio, analysis of variance (ANOVA) and analysis of means (ANOM) have been employed to identify the Taguchi results. The optimal BPNN training has been used successfully to tackle uncertain of hidden layer’s parameters structure. It has faster iterations to reach the convergent condition and it has ten times better MSE achievement than NN machine expert. The paper still shows how to use the information of car body shapes, car speed, vibration, noise, and fuel consumption of the car body database in BPNN training and validation.