System Identification of Hammerstein Model a Quarter Car Passive Suspension Systems Using Multilayer Perceptron Neural Networks (MPNN)

Recently, some researchers have focused on the applications of neural networks for system identification. In this paper, a Harnmerstein model of a quarter car passive suspension system is identified using multilayer perceptron neural networks. Input and output data are acquired by driving a car on a special road event The networks strucLure is based on system model. The network learning algorithm is based on Fisher s scoring method. Fisher information is given as a weighted covariance matrix of inputs and outputs of the network hidden layer. Unitwise, Fisher s scoring method reduces to the algorithm in which each unit estimates its own weights by a weighted least square method. The results show that the minimum mean square error (MSE) value of the training process was found with a short record.