GA优化T-S模糊神经网络的干燥窑温湿度控制器设计  

Design of T-S Fuzzy Neural Network Controller by Optimized GA for Temperature and Humidity in Drying Kiln

作　　者：姜滨[1] 孙丽萍[1] 曹军[1] 季仲致 

机构地区：[1]东北林业大学机电工程学院,黑龙江哈尔滨150040

出　　处：《实验室研究与探索》2015年第11期54-59,共6页Research and Exploration In Laboratory

基　　金：国家林业公益性行业科研专项(201304502)

摘　　要：为了准确控制木材干燥过程的温度和湿度,提高木材干燥质量,结合模糊控制、神经网络和遗传算法的优点,设计了一种遗传算法(GA)优化的T-S模糊神经网络温湿度控制器。该控制器利用模糊算法解除木材干燥窑内温度和湿度间的强耦合关系,采用神经网络的自学习和自适应能力实现整个非线性过程的模糊逻辑推理,并通过遗传算法对神经网络的参数进行优化与训练,提高系统的自学习和自适应能力。仿真实验结果表明,在木材干燥过程的温湿度控制上,GA优化的T-S型模糊神经网络控制器具有良好的控制效果,控制器响应速度快、超调小并且具有一定的鲁棒性。Wood drying process normally maintains the non-linear characteristics of strong coupling and large lagging,therefore,it is hardly to build the mathematical model of controlled object. In order to control the temperature and humidity of the wood drying process more precisely,and to improve the drying quality,a T-S fuzzy neural network（ FNN） controller was designed to control the inner temperature and humidity of wood drying kiln. The design combined the merits of fuzzy control,neural control and optimized genetic algorithm（ GA）. This controller used fuzzy algorithm to remove the coupling relationship between inner temperature and humidity of wood drying kiln. Self-learning and adaptive ability of neural network were used to accomplish the fuzzy logic of the whole non-linear process; and the parameters of neural network was optimized and trained by GA to improve the self-learning and adaptive ability of the system. The simulation results revealed that the T-S FNN controller could have better control effect,faster responding speed,lower overshoot and stronger robustness.

关 键 词：干燥过程 遗传算法 T-S模型 模糊神经网络控制器 干燥窑 

分 类 号：TP273.4[自动化与计算机技术—检测技术与自动化装置]