基于QCNN的非线性跟踪问题研究  

Research for nonlinear tracking problem based on QCNN

作　　者：牛德智[1] 陈长兴[1] 符辉[1] 赵延明[1] 屈坤[1] 王旭婧[1] 

机构地区：[1]空军工程大学理学院,西安710051

出　　处：《计算机应用研究》2013年第12期3634-3637,共4页Application Research of Computers

基　　金：陕西省电子信息综合集成重点实验室项目(201107Y16);军内武器系统科研项目(KJ2010182;KJ2012184)

摘　　要：针对如何快速准确地跟踪到非线性系统的状态问题,研究了量子细胞神经网络(QCNN)在非线性跟踪中的应用。在满足Lyapunov函数指数收敛的条件下,设计了一种新型参数形式的控制器,在此基础上,对三种非线性系统即确定性非线性运动、参数和运动规律未知的非线性数据系统以及典型蔡氏电路进行了QCNN跟踪研究。仿真结果表明,在QCNN系统中,通过设计合理的控制器可以实现非线性问题状态的有效跟踪,且实验结果为QCNN系统复杂度与跟踪的及时性之间关系提供了参考依据和有力的说明。设计的新型控制器及对实际问题处理方法为QCNN的理论及应用研究具有借鉴意义。Aiming at method of tracking status of nonlinear system quickly and timely, this paper studied application of QCNN in nonlinear tracking system. In the case that Lyapunov function was convergent in exponent rate, it designed one controller with other parameter. On the basis of this controller, QCNN controlled system tracked three kinds of nonlinear system, which were separately certain system, nonlinear data system without known parameters and its function relation, and system of Chua＇ s circuit. Simulations show that QCNN system can realize effective tracking for status of nonlinear problem by suitable controller. Furthermore,the simulation results provide reference and illustration to relationship between system＇ s complexity and accurate exhausted time of tracking. The new designed controller and disposing adopted methods in the paper can give some thinking means for QCNN theory and its application research.

关 键 词：量子细胞神经网络 非线性跟踪 LYAPUNOV函数 控制器 蔡氏电路 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]