An experimental design method for noise and vibration analysis of two car engines by feedforward and radial basis neural networks is presented. Two types of car engines are experimentally analyzed by using intelligent data acquisition card with software. Measured vibration and noise parameters of two car engines are used as desired values of the neural networks. The effectiveness of using Radial Basis Neural Network (RBNN) with backpropagation algorithm is demonstrated for predicting the vibrations and noises of two car engines. The robustness of the proposed RBNN predictor to parameters of vibration and noise as well measurement disturbances is investigated. The result of experiments and simulation show that the proposed RBNN is able to adapt effectively under disturbances.
An experimental design method for noise and vibration analysis of two car engines by feedforward and radial basis neural networks is presented. Two types of car engines are experimentally analysed by using intelligent data acquisition card with software. Measured vibration and noise parameters of two car engines are used as desired values of the neural networks. The effectiveness of using Radial Basis Neural Network (RBNN) with backpropagation algorithm is demonstrated for predicting two car engines vibrations and noises. The robustness of the proposed RBNN predictor to parameters of vibration and noise as well measurement disturbances is investigated. The result of experimental and simulation show that the proposed RBNN are able to adapt effectively under disturbances.
