基于模糊神经网络的模式识别

V. Guštin, J. Virant
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引用次数: 5

摘要

本文阐述了布尔分类器在字符码识别中的应用。二进制值用于输入和输出,而具有一组模式的电路的学习是由一些布尔神经网络中使用的改进算法完成的。模糊逻辑方法的使用为创建一种容错的、适用于各种字体和字体的字符识别理论提供了可能性。它提供了几个以不同分辨率扫描的模式示例,并使用同一组样本的一部分进行学习,这证明了模糊Boolea分类器的质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pattern recognition with fuzzy neural network

This paper explains the character code recognition with the Boolean classifier. The binary values are used both for inputs and outputs, while the learning of the circuit with a set of patterns is done by modified algorithms used in some Boolean neural networks. The use of the fuzzy logic approach offers the possibility of creating a character recognition theory which is fault-tolerant and applicable to all sorts of typefaces and fonts. It provides several examples of patterns scanned with different resolutions and learned with a part of the same set of samples which demonstrates the quality of the fuzzy Boolea classifier.

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