基于Elman神经网络的浓相输送模糊控制系统

由于粉末物料的浓相输送系统存在严重的非线性和时变性,故要想建立其准确数学模型难度非常大,本文提出了使用模糊神经网络控制系统,并对于模糊控制规则由Elman神经网络联想记忆后提取,它不但可以获得最佳控制规则,而且响应速度快并能够进行在线进行规则的修正。经仿真实验,该控制器能够对粉末物料流量在一定范围内进行协调优化时实控制。

dense phase pneumatic conveying control system based on fuzzy neural network

Owing to uncertainty and non-linearity of the dense phase pneumatic conveying system,it is very difficult to build its con-trol model .So a real-time fuzzy neural network controller is proposed in this paper,It gives a new approach to automatically gener-ate fuzzy rules by training the Elman neural network.Since the proposed adaptive controller combines the advantage of neural net-work and fuzzy reasoning,it can not only reduce the time of neural network learning but also find the optimum rule of fuzzy reason-ing.Simulation results demonstrate that the proposed controller can effectively improve tracking performance and has good real-time performance,control quality and robustness.