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An Algorithm for Fuzzy Pattern Recognition Based on Neural Networks

机译:基于神经网络的模糊模式识别算法

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摘要

According to principles of fuzzy mathematics and neural networks, a new model on neural networks, by which fuzzy patterns can be better recognized, is presented in this paper. This model combines the thoughts of neural networks and maximum membership function. Thus the insufficiency in semantic expressions of neural networks can be compensated for. In the meantime, more objective effect can be obtained than that by fuzzy pattern recognition method in fuzzy mathematics. Experimental results show that the method is valid in practical applications.
机译:根据模糊数学和神经网络的原理,提出了一种新的神经网络模型,通过该模型可以更好地识别模糊模式。该模型结合了神经网络和最大隶属度函数的思想。因此,可以弥补神经网络语义表达的不足。同时,与模糊数学中的模糊模式识别方法相比,可以获得更客观的效果。实验结果表明,该方法在实际应用中是有效的。

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