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A Combined Classifier to Detect Landmines Using Rough Set Theory and Hebb Net Learning u00026; Fuzzy Filter as Neural Networks

机译：使用粗糙集理论和Hebb网络学习的组合分类器来检测地雷 u00026;模糊滤波器作为神经网络

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摘要

Landmines are significant barrier to financial, economic & social development in various parts of the world. The demand of dependable, trustworthy, intelligent diagnostic systems in the field of landmines detection has been increasing rapidly. Metal detectors used in mine decontamination, cannot differentiate a mine from metallic debris where the soil contains large quantities of metal scrap & cartridge cases, so a device is required that will reliably confirm that the ground being tested does not contain an explosive device, with almost perfect reliability. Human experts are unable to give belief & plausibility to the rules devised from the huge databases. In this paper two combined classifiers have been discussed. In the first classifier Hebb Net learning is used with rough set theory and in the second one Fuzzy filter neural network is used with the rough set theory. Rough sets have been applied to classify the landmine data because in this theory no prior knowledge of rules are needed, these rules are automatically discovered from the database. The rough logic classifier uses lower & upper approximations for determining the class of the objects. The neural network is for training the data, and has been used especially to avoid the boundary rules given by the rough sets that do not classify the data with cent percentage probability.
机译：地雷是世界各地金融，经济和社会发展的重要障碍。在地雷检测领域中，对可靠，可信赖的智能诊断系统的需求正在迅速增长。用于地雷净化的金属探测器无法将地雷与金属碎片区别开来，因为金属碎片中的土壤中含有大量的金属屑和弹壳，因此需要一种能够可靠地确认被测地面不包含爆炸性装置的设备，完美的可靠性。人类专家无法相信从庞大数据库中设计的规则的可信性和合理性。本文讨论了两个组合分类器。在第一个分类器中，将Hebb Net学习与粗糙集理论一起使用，在第二个分类器中，将模糊滤波器神经网络与粗糙集理论一起使用。粗糙集已被用于对地雷数据进行分类，因为在该理论中不需要先验规则知识，这些规则是从数据库中自动发现的。粗略逻辑分类器使用上下近似来确定对象的类别。神经网络用于训练数据，尤其是用于避免由粗糙集给出的边界规则，这些粗糙规则不会以百分比概率对数据进行分类。

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