Wavenet Based Modeling of Vehicle Suspension System

An alternative modeling technique of vehicle suspension system which is based on an integration between wavelet theory and artificial neural network, or wavelet network (wavenet) is presented. Wavenet is a single hidden layer feedforward neural network, which uses wavelet basis function as an activation function. Polynomial windowed with Gaussian (POLYWOG) will be applied as the basis function. Wavenet parameters, such as weight, dilation and translation of the wavelet function will be optimized during its learning process, which is performed by a backpropagation algorithm. For minimizing the mean square error between model outputs and its observation, an iterative minimization method of gradient steepest descent is applied. Experimental evaluation of the proposed technique has been conducted using an input-output data collected from a running test vehicle. Observations by comparing the model responses with the actual output measurements revealed that satisfactory model matching were obtained which means that the models have captured the real basic features of the vehicle suspension dynamic characteristics