基于模糊神经网络的电弧炉控制系统及仿真

电极调节系统是电弧炉炼钢过程不可缺少的基本装备。目前常用的电极调节器大多是基于单相意识,导致电极调节中电极往往误动作,大大影响了电弧炉的运行效益。在模糊控制和神经网络的基础上,探索研究一种基于三相意识的电弧炉控制新方法,采用模糊神经网络调节器控制对电弧炉电极进行调节,控制弧流弧压稳定在一定范围之内,使电弧炉冶炼达到有功功率最大化,进一步提高电弧炉的综合运行效益,降低能耗、减轻对电网危害。并用matlab对模糊神经网络进行仿真。结果表明,本网络具有较快的训练速度和较高的泛化能力,满足电弧炉控制的要求,其控制性能优于常规电弧炉控制系统。

Electric Arc Furnace Based on Fuzzy Neural Network Control System and Simulation

Electrode regulator system is indispensable to the process of electric arc furnace steel-making basic equipment.At present most commonly used electrode regulator is based on single-phase consciousness,leading to misoperation of the electrode adjustment,greatly affected the electric arc furnace operation efficiency.This article in the above fuzzy control and neural network based on Tansuo study a prediction based on three-phase electric arc furnace control consciousness algorithm with fuzzy neural network adjustor control adjusting electrode,further enhancing the effectiveness of electric arc furnace of the Syndicated news operation,Reduce the energy consumption and reduce hazards on the grid.And using matlab to simulatate for fuzzy neural network.The results show that the network has a faster training speed and high generalization ability to meet the requirements of electric arc furnace control,the control performance is better than conventional electric arc furnace control system.