基于逻辑输入样本的神经网络算法

An Algorithm of Neural Networks Based on Logical Input Samples

本文对确定多层前向神经网络权提出了一种基于逻辑输入样本的直接算法。对于逻辑输入样本,该算法只需一个三层网络实现;否则,需在输入层和隐层间引入一个预处理层以完成输入样本向逻辑向量的转化。由于不引入误差能量函数,该算法避免了BP算法训练过程出现的收敛速度慢和误差陷入局部极小问题。本文还对该算法的正确性作了详细论证并以XOR问题解释其计算过程。

A direct algorithm to determine the weights of multi-layer feedfoward neural networks based on logical input samples is proposed in this paper. It can be realized by only three-layer networks for logical input samples; otherwise, a pretreatment layer between input layer and hidden layer has to be introduced to transform input samples into logical vectors. Because error-energy function is not introduced, the problems of slowly convergent rate and error trapped in to local minimum appearing in BP algorithm are avoided in this algorithm. Finally, the correctness of the algorithm is proved in detail and the computing process is explained with XOR problem.