谷物干燥过程模糊支持向量机控制器的设计

Design of fuzzy support vector machines controller for drying process of grain

为了准确控制谷物干燥过程的温度和湿度,设计了一种基于改进遗传算法和最小二乘算法的干燥过程模糊支持向量机控制器。利用模糊算法解除温湿度的耦合作用,采用支持向量机实现模糊逻辑控制的全过程和信号的非线线处理,同时采用混合学习算法优化控制器参数,即先采用最小二乘算法离线优化支持向量机性能参数,再采用改进遗传算法在线优化支持向量机性能参数和模糊比例因子,以使其控制性能适应对象的变化而达到最优。仿真结果表明,设计的模糊支持向量机控制器比常规PID控制器和经典模糊控制器具有更好的控制性能,能够满足谷物干燥工艺要求。

To accurately control temperature and humidity for drying process of grain, fuzzy support vector ma- chines controller for drying process was designed based on improved genetic algorithm and least square Algorithm. Fuzzy algorithm was used to decouple between temperature and humidity. Using support vector machines, fuzzy logical control of complete process and treatment of non-linear signal were realized. The controller parameters were optimized by hybrid learning algorithm. First, least square algorithm was used for off-line optimization to form support vector machines control system. Then the improved genetic algorithm was used for on-line optimization to get the optimal performance parameters of support vector machines and the optimal fuzzy proportional parameters. In the simulation, compared with the general PID controller and the traditional fuzzy controller, the results demonstrated that the fuzzy neural network controller designed gets better performance to meet the requirement of drying technology of grain.