前向多层感知器网络的逐层学习算法

提出一种改进的前向多层网络逐层学习算法，隐层神经元的输出函数由具体系统的样本输出值确定，先让前面的隐层及输入层的权值确定不变，然后对当前层的权值进行,前一隐层输出值地误差进行估计以得到新的输出值，将其作为临时教师信号用来训练前一层的权值，把每一层权值的改变量和输出值误差的估计转变为最小二乘问题，逐层处理，直到输入层，数字仿真和具体应用的结果表明了算法的有效性。

A Layer-Wise Learning Algorithm for Training Multilayer Feedforward Neural Networks

An enhanced layer wise learning algorithm for training multilayer feedforward neural networks is proposed. The output function of hidden neural units is determined by the samples of the given system adaptively. The weights of the input layer to the hidden layer are first supposed to be invariant, then the weights of the current layer are then modulated. The output values of the forward layer, which are used as temporal signals for training the weights of the forward layer, are then estimated. The calculation of the weights of every layer and the estimation of the error of the hidden layer are changed into least squares problems, and the process goes on layer by layer, up to the input layer. The results of digital simulations and applications show the effectiveness of the algorithm.