基于能量空间逼近策略的三层前馈神经网络隐层训练算法

A Hidden Layer Training Algorithm for Three-Layered Feedforward Neural Networks Based on Energy Space Approaching Strategy

针对基于最佳平方逼近的三层前馈神经网络讨论了隐层生长模式的一种训练策略 首先根据隐层输出行为和期望输出数据的分布特征对样本数据确定的N维空间进行了不同意义上的划分 分析表明最有效的隐单元其输出向量应该在误差空间存在投影分量 ,同时该分量应位于目标空间中的某一能量空间内 在此基础上提出了基于能量空间逼近策略的隐层生长式训练算法

A hidden layer growing mode training strategy is discussed for least squares approximation based three layered feedforward neural networks Firstly, according to the hidden layer output behaviors and expectation data distribution features, the N dimensional space constructed by sample data is divided into several subspaces having different significances, and it is revealed that the output vector of the most effective hidden unit should have its projective component on error space, and the component ought to be positioned in a certain energy space of target space Then a hidden layer growing mode training algorithm is proposed based on energy space approaching strategy Finally, the effectiveness of the algorithm is validated by simulation experiment