提高前馈BP神经网络字符识别效率的方法
Improving the character recognition efficiency of feed forward BP neural network
摘要：
本文采用一、二、三层隐层结构和修正的附加动量项来提高前馈反向传播神经网络的字符识别能力。本文收集了182个英文字母，并将这些字符的等价二进制矩阵形式作为训练模式应用于神经网络。当网络被训练时，连接权重在每个学习阶段被修改。对于每个训练样本，通过计算梯度下降检查误差表面的最小值。我们通过使用一个隐藏层开始实验，隐藏层的数量增加到三个，并且已经观察到网络的准确性以低均方误差增加，但以训练时间为代价。通过引入修正的附加动量项，进一步提高了识别精度。
英文摘要：
This work is focused on improving the character recognition capability of feed-forward back-propagation neural network by using one, two and three hidden layers and the modified additional momentum term. 182 English letters were collected for this work and the equivalent binary matrix form of these characters was applied to the neural network as training patterns. While the network was getting trained, the connection weights were modified at each epoch of learning. For each training sample, the error surface was examined for minima by computing the gradient descent. We started the experiment by using one hidden layer and the number of hidden layers was increased up to three and it has been observed that accuracy of the network was increased with low mean square error but at the cost of training time. The recognition accuracy was improved further when modified additional momentum term was used.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
