等效分组级联BP网络模型及其应用

Equivalent Grouping-Cascaded BP Network Model and Its Applications

松弛的和紧密的分组级联BP网络模型等概念的提出,对于解决有限的小样本情况下高维BP网络的训练和预测问题有一定的参考价值.定义了BP网络等效性和相关定理,构建并证明了与BP网络等效的分组级联网络模型,分析比较了两种网络模型所需训练样本的数量情况,并将其应用于网络安全评估领域.最后通过仿真试验结果证实了所提出分组级联BP网络模型的可行性和有效性.

This paper puts forward to the concept of loosely and tightly grouping-cascaded BP network model to solve the training problem of high dimension BP neural network with limited small samples.After defining the equivalence definition and relative theorem of BP neural network,the grouping-cascaded model which is proved to be equivalent to BP network is constructed and the required number of training samples between them is compared.Then this method is applied in the evaluation of the network security.Finally,the feasibility and validity of the proposed grouping-cascaded BP network model is verified with simulation results.