【摘要】 根据直流电弧炉电弧电压信号所具有的混沌特性,使用相空间重构理论和径向基函数神经网络对直流电弧炉进行了建模和预测研究。先计算了电弧电压时间序列的最大Lyapunov指数来验证电弧电压信号的混沌特性,后对所测得的电弧电压信号进行相空间重构,根据所求得的嵌入维数和延迟时间,使用径向基函数神经网络对电弧电压信号建模和预测。分析表明:该法用于直流电弧炉电气特性的建模研究,且进一步提高了预测的准确性。同时,多步预测结果可为直流电弧炉电弧电压的有效控制提供理论上的参考依据。更多还原

【Abstract】 In the paper,research of the modeling problem of DC electric arc furnace(EAF) is presented.Based on the chaotic characteristic of the electrical fluctuations in the arc furnace voltage,the modeling and prediction of DC EAF using the phase space reconstruction theory and neural network is studied.First,the largest Lyapunov exponent of arc voltage waveform is calculated to confirm the chaotic behavior of arc furnace.Then a reconstructed phase space is obtained and the delay time and embedding dimension are calculated.Radial basis function neural network is applied to predict the arc voltage of arc furnace based on the calculated embedding dimension.It is showed by analysis that the proposed method can be applied to the modeling problem of DC EAF and shows better result than the previous method.Meanwhile,the prediction results of multi-step can also be used as a theoretical reference for the effective control of arc voltage.更多还原