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Neural networks with excellent abilities in learning and recognition problems for complex patterns

神经网络在复杂模式的学习和识别问题方面具有出色的能力

基本信息

批准号：
15500150
负责人：
FUJIKI Nahomi M.
金额：
$ 0.64万
依托单位：
Sendai National College of Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2003
资助国家：
日本
起止时间：
2003 至 2004
项目状态：
已结题

项目摘要

Error back propagation is a well-known and powerful supervised learning algorithm for feed-forward multi-layered neural networks, but it is known to introduce the problem of its learning process being often trapped in a meta-stable state. It has been reported that the error back propagation algorithm with Kullback divergence instead of conventional quadratic error as an error measure demonstrates an excellent learning tendency and is expected to be applicable to various problems, even without the foreknowledge of their optimal network size.We have studied this algorithm carefully based on the identification problem of handwritten numerical characters and discussed its abilities such as scalability, flexibility, damage tolerance, and recognition of unlearned data. The numerical studies have been done on the three-layered feed-forward network ; an input layer, a hidden middle layer with various numbers of neurons up to 8129, and an output layer. Results of numerical studies indicate that this learning algorithm can have high generalization ability and be sufficiently powerful for practical use. Those network abilities are also improved by increasing its size. If we prepare an enough size of network, we could easily obtain the identification rate of higher than 99% after a few learning steps and recognition rate for unlearned characters over 90%.Those research results have been published on several papers in Research Reports on Sendai National College of technology, and also presented the papers at various conferences such as Tohoku-section Joint Convention of Institutes of Electrical And Information Engineers.

误差反向传播是一种著名的、功能强大的前馈多层神经网络有监督学习算法，但其学习过程经常陷入亚稳态。以Kullback发散度代替传统的二次误差作为误差度量的误差反向传播算法表现出了良好的学习倾向，即使在不知道最优网络规模的情况下也有望适用于各种问题，我们基于手写数字字符识别问题对该算法进行了仔细的研究，讨论了该算法的可扩展性、灵活性、容损性和对未学习数据的识别能力。对前馈网络的三层结构进行了数值研究：输入层、隐含中间层和输出层。数值研究结果表明，该学习算法具有较强的泛化能力，具有较强的实用价值。这些网络能力也通过增加其规模而得到改善。如果我们准备一个足够大的网络，经过几个学习步骤就可以很容易地获得99%以上的识别率和90%以上的未学习字符识别率。这些研究成果已经在仙台国立理工学院的研究报告上发表了多篇论文，并在东北地区电气与信息工程师学会联席会议上发表了论文。