Full-Car Active Suspension System Identification Using Flexible Deep Neural Network

Abstract

This paper presents the system identification based on a flexible deep neural network for a seven degree of freedom(7DOF), a full-car active suspension system that is multi-input and multi-output. The proposed flexible deep neural network, according to input and output data, obtained three layers of flexible auto-encoder. The flexible name was chosen for the learnable activation function parameter in the activation layers. This view permits every neuron to adjust its activation function and adapt the neuron to boost performance. Here flexible tanh activation function introduced, which causes better performance with the same neurons in the hidden layer. The comparison shows the identification error between flexible deep neural network and classical deep neural network. This adaptation, of course, provides prediction improvement.