具有类所属粒化的颗粒神经网络模型

D. A. Kumar, S. Meher
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引用次数: 5

摘要

颗粒神经网络(GNNs)将模糊颗粒化输入通过神经网络进行处理。因此,gnn的性能在很大程度上取决于造粒过程和神经网络的初始权重。gnn节点间的初始权值是搜索最小代价函数值的起点。本文提出了使用类归属模糊粒化输入信息和粗糙集理论权重初始化神经网络的GNN模型。因此,该模型避免了权重的随机初始化,并通过CB粒化在输出处提供了改进的决策。利用各种度量指标对所提出的GNN模型的分类性能进行了评价,证明了其优于同类方法的优越性。采用传统的反向传播算法对所提出的GNN模型进行训练。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Granular neural networks models with class-belonging granulation
Granular neural networks (GNNs) take the fuzzy granulated input and process them through neural networks (NNs). As a result, performance of GNNs depends highly on the granulation process and initial weights of NNs. The initial weights between nodes of GNNs provide the starting point in the searching of the lowest cost function value. The present article proposes GNN model that use class-belonging (CB) fuzzy granulation of input information and rough set-theoretic weight initialization of NNs. The model thus avoids the random initialization of weights and provides improved decisions at the output with CB granulation. Classification performance of the proposed GNN model has been assessed using various measurement indexes and its superiority over similar other methods is justified. Conventional back propagation algorithm is used to train the proposed model of GNN.
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