摘要
在过程控制中 ,由于被控对象常具有非线性、不确定性及参数时变等复杂因素 ,难以建立精确的数学模型 ,从而直接影响了控制效果 .提出了一种模糊神经网络自适应预测控制方案 ,对学习公式进行了理论推导 ,并结合误差补偿以提高预测控制的精度 .仿真实验表明 ,该算法可实现模糊控制和神经网络的优势互补 。
In the process control,due to some complex factors of the controlled object with nonlinearity,uncertainty and time varying parameters,it is difficult to construct an accurate mathematical modal,and therefore the control effect is affected.In this paper an adaptive predictive control algorithm based on fuzzy neural network has been developed,and a new learning algorithm was derived theoretically.Based on the error compensation method,the accuracy of the algorithm was improved.Experiment results show that the algorithm can obtain the advantages of the fuzzy system and the neural network can have better performance in controlling the nonlinear and complex system.
出处
《天津大学学报（自然科学与工程技术版）》
EI
CAS
CSCD
2000年第4期428-431,共4页
Journal of Tianjin University：Science and Technology
关键词
模糊控制
神经网络
自适应预测控制
fuzzy control
neural network
fuzzy neural network
adaptive predictive control
