基于T-S模糊神经网络分解炉燃烧控制系统设计
    点此下载全文
引用本文:李 涛,梁 凯,高若尘,申 琦,张慧杰,宜 文.基于T-S模糊神经网络分解炉燃烧控制系统设计[J].计算技术与自动化,2017,(4):33-36
摘要点击次数: 250
全文下载次数: 
作者单位
李 涛,梁 凯,高若尘,申 琦,张慧杰,宜 文 (湖南大学 信息科学与工程学院湖南 长沙 410082) 
中文摘要:针对分解炉分解是非线性、大滞后、多扰动及多变量过程,难以实现其对温度自动控制的问题,提出了一种基于T-S神经网络的控制方法。针对这一问题,文章首先对水泥预分解工艺进行分析以及对燃烧理论进行研究,然后再利用T-S模糊控制理论确定规则数目和输入变量的隶属度函数,采用神经网络的自学习和自适应能力实现模糊推理。仿真结果表明:该控制器对分解炉燃烧控制起到很好的控制效果,并且比传统PID控制器具有更好的效果。在实际生产应用当中,具有很好地稳定性和鲁棒性,并且节省了煤的消耗和降低了环境污染。
中文关键词:分解炉  模糊神经网络  T-S模糊  控制
 
Optimal Combustion System Design Based on T-S Fuzzy Neural Network Decomposing
Abstract:A new control method based on T-S neural network is proposed to solve the problem that the decomposition of the calciner is nonlinear,large-lag,multi-disturbance and multivariable process,and it is difficult to realize the automatic control of temperature.In order to solve this problem,this paper firstly analyzes the process of cement pre-decomposition and studies the combustion theory,then uses TS fuzzy control theory to determine the number of rules and the membership function of input variables,using neural network self-learning and self-adaptability Fuzzy reasoning.The simulation results show that the controller has a good control effect on the combustion control of the precalciner and has a better effect than the traditional PID controller.In the actual production applications,with good stability and robustness,and save the consumption of coal and reduce environmental pollution.
keywords:decomposition furnace  neural network  T-S fuzzy  control
查看全文   查看/发表评论   下载pdf阅读器