干燥过程模糊神经网络控制器的设计与仿真

Design and Simulation of Fuzzy Neural Network Controller for Drying Process

为了准确控制干燥过程的温度和湿度,提高谷物干后品质,设计了一种基于改进遗传算法的干燥过程模糊神经网络控制器。利用模糊算法解除温湿度的耦合作用,采用神经网络实现模糊逻辑控制的全过程和信号的非线形处理,将训练好的被控对象网络模型与模糊神经网络控制器联成闭环回路,应用改进遗传算法对模糊神经网络控制器的参数进行自适应调整。在仿真实验中,将所设计的控制器与常规PID控制器和经典模糊控制器进行比较,结果表明所设计的模糊神经网络控制器具有较好的性能,满足了谷物干燥过程的控制要求。

To accurately control the temperature and humidity of drying process and improve the quality of dried grain, fuzzy neural network controller for drying process was designed based on improved genetic algorithm. Fuzzy algorithm was used to decouple between temperature and humidity. Using the neural network, fuzzy logical control of complete process and treatment of non-linear signal were realized. Fuzzy neural network controller was linked with the trained network model for controlled object. Improved genetic algorithm was used to adaptively regulate the parameters of the fuzzy neural network controller. In the simulation, compared with the general PID controller and the traditional fuzzy controller, the results demonstrate the fuzzy neural network controller designed gets better performance to meet the requirement of drying technology of grain.