This book is intended for senior undergraduate or post graduate in control system engineering. This book is based on the author research and study experiences. This book covers topics on neural network, car suspension system, vibration dynamic model, experimental method and system identification. It is aimed to give overview a quarter car modelling using intelligent system identification. This book is organized in 6 chapters as below:

Chapter 1 briefly explain the concept of model, modeling by system identification with experimental modeling motivation and system identification and at the end represent a car suspension system.

Chapter 2 presents two degree of freedom (dof) car’s passive suspension model. A quarter car physical model and mathematical model are explained. This explanation includes a description of the various physical model of a quarter car suspension system, components of the suspension system and the use of a quarter car mathematical model. Finally represents the derivation method of the Hammerstein model of a quarter car passive suspension system.

Chapter 3 presents an introduction of neural networks, including the historical background of neural networks and concepts of neural networks. The neural networks concepts consist of the concept and biological of neuron, the network architecture, a learning algorithm, and learning rate. Beside, this chapter also described the neural networks learning laws, selection and preparation of training data, activation function, characteristic of artificial neural networks, and Multilayer Perceptrons. At the end of this chapter the Multilayer Perceptron subchapter the training of the Multilayer Perceptron and Backpropagation networks is presented.
 
Chapter 4 provides an explanation on the hardware and experimental setup. The hardware consists of the sensors, data acquisition system (DAS), the test car, a computer, and an artificial road surface. The end of this chapter presents the method of processing the raw data.

Chapter 5 deals with the system identification perspective on neural networks, including an overview of the neural network training algorithm and neural network system identification. A new method in developing the Multilayer Perceptron training algorithm is proposed and verified. 

Chapter 6 presents the application of the method in chapter 5 to identify the several nonlinear models of a quarter car passive suspension system. This chapter starts with the method properties and investigates the effect of the activation function and system model for system identification results.