基于粗糙集神经网络在模式识别中的应用VIP免费

基于粗糙集的神经网络在模式识别中的应用摘要： 为解决神经网络在模式识别中存在的噪声问题，基于粗糙集的上、下近似和边界线集理论提出了一种对噪声样本进行处理的方法。该方法主要包括对处于下近似集内的含噪声属性值 ,将噪声消除后转换为理想状态下的属性值;对处于边界域内的含噪声属性值保持不变。当属性值处于边界域内属性的个数与全部属性数的比值达到某个确定的值时,就认为该样本受到噪声干扰过大,对其拒绝识别。通过实验对比表明,该方法能有效地降低BP 网络模式识别的误识率。关键词： 粗糙集神经网络模式识别中图分类号： TP18 文献标识码： A Study on BP Network for Pattern Recognition Based on RS Theory Fan li meng ( School of Information Engineering, Hebei University of Technology, Tianjin 300401, China ) Abstract: In order to solve the noise problem of BP network for pattern recognition,proposes a method to process the noisy samples based on the upper approximations, the lower approximations and the boundary region theories of rough sets. The method eliminates the noise of attribute values and changes them into ideal values when they are in the lower approximations; and those attribute values with noise will remain unchanged while they are in the boundary region. The sample will be refused to recognize if the percent of its attributes with their values in the boundary region is over a certain point. The results of experiment show that the method can effectively reduce the false recognition rate of BP network for pattern recognition.Keywords: rough sets,BP network,pattern recognition1 前言神经网络在模式识别中的应用十分广泛，由于网络训练样本中存在大量的冗余信息，常导致神经网络结构复杂、训练速度较慢、识别率不高等问题。粗糙集理论是一种处理含糊和不精确性问题的新型数学工具。自1982年波兰科学家Z.Pawlak 提出该理论以来，发展十分迅速。粗糙集理论具有强大的定性分析能力，不需要预先给定某些特征或属性的数量描述，能有效地分析和处理不精确、不完整、不一致...