中文摘要：

针对传统对传神经网络（Counter propagation networks，即CPN）要求输入向量必须均匀分布以及隐含层神经元个数难以确定，其应用受到很大局限等问题，对CPN算法进行改进并运用于电力负荷预测。研究结果表明：通过改进CPN算法的初始权重设置规则，克服了对输入向量限制过于严格的不足；通过优化算法运行步骤，提高了算法的运行效果；改进后的CPN算法比BP算法所得预测结果误差小，比日前电力负荷预测研究中RBF和Elman神经网络所得预测结果误差也小；与BP算法相比，CPN改进算法的预测精度提高4％左右，运算时间减少45％，适用丁电力负倚的预测。

英文摘要：

Based on the fact that the input vectors of counter propagation networks（CPN） are supposed to be uniform distribution,, and it is difficult to choose the number of their hidden lay neurons, its application is restricted in a few fields, CPN algorithm was improved and was applied to power load forecasting. The results show that through changing the setting rules of the initiation weight, the problem of too strict limitation to input vectors can be solved. Based on the optimization of the operation process, the efficient of the CPN can be enhanced. The simulation error using the ameliorated CPN is lower than those with BP, RBF and Elman networks. Compared to traditional BP networks, the forecasting accuracy using the ameliorate ameliorated PN improves about 4%, and the computing time reduces 45%. The improved CPN can be used to forecast the power load.