基于遗传算法的细胞神经网络自动设计:寻找特征检测器

Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94) Pub Date : 1994-12-18 DOI:10.1109/CNNA.1994.381681

F. Dellaert, J. Vandewalle

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引用次数: 14

摘要

本文旨在研究使用遗传算法来优化细胞神经网络体系结构的子系统。当前的应用是字符识别:目的是发展一个最优的特征检测器，以帮助传统的分类器网络泛化不同的字体。为此，提出了特征检测器的性能函数和遗传编码。本文描述了一个用遗传算法找到最优特征检测器的实验

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Automatic design of cellular neural networks by means of genetic algorithms: finding a feature detector

The paper aims to examine the use of genetic algorithms to optimize subsystems of cellular neural network architectures. The application at hand is character recognition: the aim is to evolve an optimal feature detector in order to aid a conventional classifier network to generalize across different fonts. To this end, a performance function and a genetic encoding for a feature detector are presented. An experiment is described where an optimal feature detector is indeed found by the genetic algorithm.<>

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Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)

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