System identification of hammerstein model a quarter car passive suspension systems using Multilayer Perceptron Neural Networks (MPNN)

Abstract

Recently, Some Researchers Have Focused On The Applications System Identification. In This Paper, A Hammerstein 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 Structure 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.