神经网络训练的分解重组算法

本文介绍了把线性不可分问题分解为一系列线性可分子问题、对线性不可分问题进行求解的网络分解重组算法．还证明了该算法的收敛性．实例研究表明：该算法不仅可以得到神经网络的隐层空间目标和隐层单元数，而且提高了对线性不可分问题的求解速度，因此是一个非常有效的神经网络训练算法．

Decomposition and Combination Algorithm for Training Neural Networks

The network decomposition and combination algorithm is proposed, with which a nonlinearly separable problem can be decomposed into several linearly separable sub problems, which can be easily realized by sub nets. Then the sub nets are combined to form a network, which can be efficiently trained to solve the nonlinearly separable problem. This algorithm's convergence is proved. Some example studies were carried out. It was shown that this algorithm, which could be used to obtain hidden objects and determine number of hidden units in the hidden layer, is a very efficient and fast algorithm for training neural networks.