Abstract
Previous research works tried to optimize the architectures of Back Propagation Neural Netwo rks (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 o f BPNN car body design system. The paper started with literatures review to define factors and level of BPNN parameters for number of hidden layer, nu mber of neurons, learn ing algorithm, and etc. Then the BPNN arch itecture is optimized by Taguchi method with Mean Square Error (MSE) indicator. The Signal to No ise (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 h idden 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 informat ion of car body shapes, car speed, vibration, noise, and fuel consumption of the car body database in BPNN training and validation.
Highlights
The back propagation neural network (BPNN) is widely used in industry, military, finance, etc
In the BPNN, controllable experiment factors of the Taguchi method include the number of neurons in each h idden layer, transfer function, nu mber of hidden layers, epoch, etc
The discussion will divide into 4 steps for: 5.1. introduction the learning algorith m, 5.2. test BPNN without Genetic algorith m (GA) in intelligent car body training, 5.3. test BPNN with GA applicat ion in intelligent car body training, and 5.4. co mparison the optimu m BPNN training with or without GA applicat ion
Summary
The back propagation neural network (BPNN) is widely used in industry, military, finance, etc. John F.C. Khaw and friends[4] investigated the optimal design of neural network using the Taguchi Method. Khaw and friends[4] investigated the optimal design of neural network using the Taguchi Method They worked in NN’s parameters of nu mber of h idden layer and number of node in hidden layer. The paper shows manual NN training co mparisons based on different transfer function and training dataset. It noted that dataset is important part to obtain a better MSE performance. Th is paper shows how emp loy GA to adjust three learning algorith ms; Conjugate Grad ient(CG), Delta–Bar–Delta (DBD) and Quick Propagation for weights adjustment during the BPNN training
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