**9. References**


**6** 

*China* 

**Recurrent Neural Network-Based Adaptive** 

*College of Computer Science and Technology, Dalian University Of Technology, Dalian,* 

The design goal of a control system is to influence the behavior of dynamic systems to achieve some pre-determinate objectives. A control system is usually designed on the premise that an accurate knowledge of a given object and environment cannot be obtained in advance. It usually requires suitable methods to address the problems related to uncertain and highly complicated dynamic system identification. As a matter of fact, system identification is an important branch of research in the automatic control domain. However, the majority of methods for system identification and parameters' adjustment are based on linear analysis: therefore it is difficult to extend them to complex non-linear systems. Normally, a large amount of approximations and simplifications have to be performed and, unavoidably, they have a negative impact on the desired accuracy. Fortunately the characteristics of the Artificial Neural Network (ANN) approach, namely non-linear transformation and support to highly parallel operation, provide effective techniques for system identification and control, especially for non-linear systems [1-9]. The ANN approach has a high potential for identification and control applications mainly because: (1) it can approximate the nonlinear input-output mapping of a dynamic system [10]; (2) it enables to model the complex systems' behavior and to achieve an accurate control through training, without a priori information about the structures or parameters of systems. Due to these characteristics, there has been a growing interest, in recent years, in the application of

"Depth" and "resolution ratio" are the main characteristics to measure the dynamic memory performance of neural networks [11]. "Depth" denotes how far information can be memorized; "resolution ratio" denotes how much information in input sequences of neural networks can be retained. The memory of time-delay units is of lower depth and higher resolution ratio, while most recurrent neural networks, such as Elman neural networks, are higher depth and lower resolution ratio. The popular neural networks have much defect on dynamic memory performance. This chapter proposed a novel time-delay recurrent network model which has far more "depth" and "resolution ratio" in memory for

neural networks to dynamic system identification and control.

**1. Introduction** 

 \*

Corresponding author

**Controller Design for Nonlinear** 

**Dynamical Systems** 

Hong Wei Ge and Guo Zhen Tan\*

