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粗糙RBF神经网络集成的模式识别方法

, PP. 609-614

Keywords: 神经网络集成,径向基函数(RBF)神经网络,粗糙集,广义重要度,模式识别

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Abstract:

提出一种定义属性重要度的方法,并根据属性的重要度测量元素之间的距离,以确定训练集的聚类情况.由于聚类的不确定性,提出利用粗糙集方法确定精确的下、上近似集合,用其聚类中心作为RBF神经网络的径向基中心,设计两个基函数中心不同的RBF神经网络.最后在经验风险最小化原则下,确定两个网络的每个输出值的置信度,得到神经网络集成的最终输出.网络的训练采用递推最小二乘方法,通过两个模式识别仿真实例验证该方法的有效性和正确性.

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