氧乐果合成反应温度的模糊神经网络控制方法

氧乐果合成反应温度控制过程具有参数时变、非线性、大滞后等特点,采用传统的控制方法难以达到预期的控制效果。针对此问题,文章设计了基于遗传算法的模糊神经网络控制器。首先确定与模糊系统结构等价的神经网络,从而将模糊控制规则和隶属函数的参数搜索优化问题转化成网络参数优化问题;其次利用改进的遗传算法实现网络参数快速全局寻优,从而提高控制器性能。仿真结果表明,优化后模糊神经网络控制器对一类具有参数时变、时滞、非线性的系统具有良好的控制性能。

Fuzzy neural network controller for temperature of omethoate synthesis system

The control of the temperature of omethoate synthesis has characteristics of time-varying parameters,nonlinearity and lag.It is difficult to meet the expected control effect with the common control method.To solve this problem, this paper puts forward a kind of fuzzy neural network controller optimized by genetic algorithm.First,it uses neural network to construct fuzzy logic system according to the structure equivalence rule,thus the optimization of fuzzy control rules and membership functions can be realized by finding the weight value of the neural network.Then,it uses the improved genetic algorithm to find the global optimum weighted factors with a high speed so to improve the performance of the controller.The simulation results show that the optimized fuzzy neural network controller can obtain an excellent control performance for the nonlinearity system with time-varying parameters,and lag.