摘要
由于噪音、负载扰动等环境条件的变化,过程控制参数及模型结构往往会发生变化。为了提高控制器的性能,通过自适应神经元学习来修改模糊控制规则的控制方法。它通过总结过去控制规则的控制性能,对当前的控制规则进行调整,使之适应环境的变化,改善当前过程控制的输出。经仿真与实际检验,效果良好。
The parameter and model structure of a control system always change when the working condi-tions such as noise vary. Many methods have been found to solve this problem, but their results are not al-ways satisfactory due to their complexity and impractical. To increase control system adaptability and raise the quality of the process control output , a new method of learning fuzzy control rules using adaptive neural element is described. The method presented can generate and modify the fuzzy control rules which act on pro-cess previously and affect the current performances of control system. The computer simulation result is sat-isfactory. The method has been used in a two dimension numerical control plotter, and the practical result is also good.
出处
《控制与决策》
EI
CSCD
北大核心
1997年第3期269-273,共5页
Control and Decision
关键词
自适应神经元
模糊控制
模糊推理
fuzzy control, adaptive neural element learning,approximate reasoning
