英语翻译A phase space is reconstructed from the scalar time seri

英语翻译
A phase space is reconstructed from the scalar time series representing the chaotic characteristics of electricity price.The main features of thee attractors are extracted and the surrogate data method is used.Global and Local price forecasting model based recurrent neural network is proposed and applied for the New England Market..An artificial intelligence method using both fuzzy C- means (FCM) algorithm and recurrent neural network (RNN) is used for forecasting the LMPs.The RNN were trained using historical prices for two months from Pennsylvania,New Jersey and Maryland (PJM).The RNN was found to forecast electricity prices with reasonable amount of accuracy.
System marginal price short term forecasting (48 h) using a three layer artificial neural network employing data from Victorian power system is presented [9].Model sensitivity test for input variable selection validation is discussed to justify the concept of influence of input variables on the output.Electricity price forecasting based on chaos theory is presented which is based on the fact that electricity price possesses chaotic characteristics,where the Lyapunov and components and fractal dimensions of the attractors are extracted.An accurate phase space is reconstructed by multivariable time series constituted by electricity price and its correlated factors.A recurrent neural network is employed for Global and Local electricity price forecasting.

相空间是从标量时间代表电价的混沌特性序列重构.你那里吸引的主要特点是提取和使用替代数据方法.全局和局部价格预测模型的递归神经网络提出并实现了新英格兰的市场应用. .人工智能方法同时使用模糊C -均值（FCM）算法和递归神经网络（RNN的）可以预测LMPs使用.该RNN的使用进行了培训两名宾夕法尼亚州,新泽西州和马里兰州（宾新马）个月的历史数据.被发现的递归神经网络预测的准确性与合理数量的电力价格.
系统边际价格短期预报（48小时）,采用三层人工神经网络采用来自维多利亚时代的电力系统数据,提出了[9].模型的输入变量选择验证药敏试验证明,讨论了输入对输出变量的影响的概念.电价预测基于混沌理论的基础上,提出这是一个事实,即电价具有混沌特性,其中的李雅普诺夫
和组件和吸引子分形维数提取的基础.准确的相空间重构的多变量时间序列的电力价格及相关因素构成.神经网络是经常性雇用的整体和局部电价预测.