摘要
针对焦炉立火道温度系统的复杂多变等特性,采用自适应神经模糊推理系统(ANFIS)方法中所具有的模糊经验知识和神经网络中的自学习功能等优点,解决焦炉温度模型高耦合性、多变性和不确定性问题,进而建立ANFIS辨识模型。同时,在模糊控制器中引进遗传优化算法,实时调整模糊隶属度规则,以达到平稳、快速、准确的控制要求。Matlab仿真证明了该方案的可行性和有效性。所设计的方案将为以后焦炉立火道温度的控制优化的研究提供理论性的指导。
In view of the complex and changeable characteristics of the vertical flame path in coke oven, by adopting the fuzzy experience knowledge in adaptive neural fuzzy inference system （ANFIS） and the self-learning function in neural network, the strong coupling, variability and uncertainty of the temperature model of coke oven can be solved, and then the ANFIS recognition model is established. In addition, the genetic optimization algorithm is introduced in fuzzy controller, to adjust the fuzzy membership rules in real time to achieve smooth, rapid and accurate control. The Matlab simulation proves that the scheme is feasible and effective. Finally the scheme designed will provide theoretical guidance for optimization research of coke oven vertical flame path temperature control.
出处
《自动化仪表》
CAS
2015年第4期75-78,83,共5页
Process Automation Instrumentation
基金
国家自然科学基金资助项目(编号:61203021)
