【Key words】 Short Term Load Forecasting； Support Vector Machine； Structural Risk Minimization； Statistical Learning Theory； online training algorithm；
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提出了关键技术， 包括：挖掘主题的定义方法、海量训练样本的在线生成和高性能数据挖掘算法。
The key technologies is proposed, including methods of definition of mining topics, online acquirement of extra large amount of training samples, and algorithms of data mining with high performance.
当训练样本线性可分时，本文证明前馈神经网络的在线BP算法是有限次收敛的。
In this paper we prove a finite convergence of online BP algorithms for nonlinear feedforward neural networks when the training patterns are linearly separable.
应用神经网络的误差反向传播算法(BP)和大量的实测数据样本训练出了能在线诊断四种加工状态的BP模型并成功地诊断了实际加工状态。
The BP algorithm of Artificial Neural Networks and lots of experimental samples were used in training the BP model which succeeded in diagnosing four kinds of operational status.
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