摘要
非线性、大滞后系统是工业生产过程中普遍存在的现象,被认为是物理系统中最难控制的重要环节。本文以水煤气生产过程为背景,对这类复杂非线性系统提出了一种基于改进B-P神经网络的预测控制方法,仿真结果表明该方法的有效性和快速性,从而为非线性、大滞后系统的实时智能优化控制的实现提出了一种有效的方法。
The nonlinear system with time-delay generally exists in the industry procedure, which isregarded as the most difficulty control. In the background of coal gas produce process, thepaper proposes a predictive control method based on improved B-P neural network tononlinear and time-delay system. The methods simulation experiment and application re-sult show its validity. So the paper proposes a better real time intelligence optimizationcontrol means to nonlinear and time-delay system.
出处
《制造业自动化》
2004年第2期26-31,共6页
Manufacturing Automation
基金
国家"八五"重点科技项目(85-207-22-01)
关键词
煤气生产过程
神经网络
预测控制
数学模型
非线性函数
the nonlinear system with time-delay
predictive control
B-P neural network
circulatinginterval water gas making process
