一种基于特征的人工神经网络数字识别系统模型

在进行神经网络方法的数字图像识别研究时，针对数字图像的复杂性、神经网络输入数据量巨大和学习收敛速度慢的情况，提出了基于特征的神经网络数字识别方法。设计提取了13个结构特征以组成一个13维的超空间，对13维的超平面空间的整个识别过程采用了神经网络聚类方法(其中聚类数为10)，并利用数学软件MATLAB强大的矩阵运算能力和C语言高效的代码实现了数字识别系统模型。实验结果表明，在特征差异不十分明显的情况下，识别时间和识别率仍令人满意。

A System Model of Artificial Neural Network Figure Recognition Based on Features

This paper proposes a figure character recognition method based on features aiming at the complexity of character image,a large quantitity of input data and the slow convergence speed in recognizing and researching character image.Thirteen construction features designed and extracted in this paper compose a thirteen-dimension hyperplane space,wherein a character recognition system model is developed using the neural network cluster method(10 cluster number),the MATLAB with powerful matrix operating ability and the efficient code of C Language.Experiment results indicate that the recognition time and rate are still satisfactory under the indistinctive feature differences.