摘要
针对电弧炉电极调节系统,建立其数学模型。分析了电弧炉电极调节系统的非线性,并针对控制对象的复杂性,将具有自学习功能的BP神经网络与模糊控制相结合,提出了基于BP神经网络模糊控制的控制算法。BP神经网络模糊控制的控制算法改善了传统神经网络学习时间长、收敛速度慢的弱点,解决了传统控制未知复杂系统的不足,Matlab6.5软件仿真结果表明,采用BP神经网络模糊控制的控制算法的控制效果是令人满意的。
Build the dynamic mathematics model of the electrode regulator system of arc furnace. The nonlinearity of the electrode regulator system are analyzed. Combination of the widely used fuzzy controller and the self-learning neural network, this paper introduces neural network fuzzy control algorithm based on the BP network, aiming at the complex system. The BP neural network fuzzy control algorithm improves the traditional neural network which has the weakness of long learning time and slow convergence. Also solves the deficiencies of unknown control of the traditional complex systems. Matlab6. 5 software simulation results show that the control result after optimized by the neural network fuzzy control algorithm based on the BP network control is satisfactory.
出处
《电气自动化》
2010年第3期18-20,共3页
Electrical Automation
基金
国家高技术研究发展计划(863计划)项目(2007AA041401)
天津市自然科学基金项目(08JCZDJC18600
09JCZDJC23900)
关键词
电弧炉
非线性
神经网络模糊控制的控制算法
电弧炉电极调节系统
electrode arc furnace nonlinearity neural network fuzzy control algorithm electrode regulator system of arc furnace
