摘要
介绍了一种基于模糊B样条基函数神经网络的控制器，该控制器将模糊控制的定性知识表达能力、神经网络的定量学习能力和B样条基函数优异的局部控制性能相结合，采用B样条基函数作为模糊隶属函数。还提出了模糊神经网络控制器的混合学习算法，即先采用免疫遗传算法离线优化，再采用BP梯度算法在线调整。对锅炉主蒸汽温度控制的仿真结果表明了此法的可行性和有效性。
A novel controller based on the fuzzy B-spline neural network is being presented, which combines the advantages of qualitative defining capability of fuzzy logic, quantitative learning ability of neural networks and excellent local controlling ability of B-spline basis functions, which are being used as membership of fuzzy functions. Simultaneously, a hybrid learning algorithm of the controller is proposed as well, in which immune genetic algorithm is used offline first for optimizing, followed by online adjustment with BP algorithm. Simulation results of a boier＇s fresh steam temperature control shows the method to be feasible and effective. Figs 4 and refs 3.
出处
《动力工程》
CSCD
北大核心
2005年第3期358-358,共1页
Power Engineering
基金
上海市教委资助项目
关键词
B样条基函数
模糊神经网络
混合学习算法
控制系统
锅炉
automatic control technique
intelligent control
fuzzy B-spline neural network
hybrid training algorithms
main steam temperature
