自组织模糊多项式神经网络-多阶段分类器

N. Mitrakis, J. Theocharis
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引用次数: 1

摘要

本文提出了一种适用于处理大特征空间的复杂分类问题的模糊多项式神经网络多阶段分类器(FPNN-MC)。多层FPNN-MC结构采用自组织方式,利用结构学习过程进行开发。网络的神经元通过基于模糊规则的TSK系统实现,被认为是通用模糊神经元分类器(FNC)。父FNC被组合起来,在随后的层开发新的更高级别的后代分类器。因此,实现了顺序多阶段决策,提高了分类结果。为了利用FNC在每一层获取的信息,实现有效的数据流,开发了一种与数据约简机制相关联的融合方案。在结构构建结束后,利用遗传算法平台进行参数学习。该方法的一个显著优点是它解决了特征选择任务,提供了与问题最相关的特征。对一个已知分类问题的仿真结果表明了该模型的有效性
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Self-Organizing Fuzzy Polynomial Neural Network - Multistage Classifier
A fuzzy polynomial neural network multistage classifier (FPNN-MC) is suggested in this paper, suitable for handling complex classification problems with large feature spaces. The multilayered FPNN-MC structure is developed in a self-organizing way, using a structure learning procedure. The network's neurons are realized through fuzzy rule-based TSK systems, considered as generic fuzzy neuron classifiers (FNC's). Parent FNC's are combined to develop new higher-level descendant classifiers at the subsequent layer. Hence, sequential multistage decision is implemented, leading to improved classification results. To exploit the information acquired by FNC's at each layer and achieve an effective data flow, a fusion scheme is developed associated with a data reduction mechanism. Upon termination of the structure building, parameter learning is carried out using a genetic algorithm platform. A remarkable asset of the approach is that it resolves the feature selection task, providing the most relevant features of a problem. Simulation results on a well known classification problem indicate the efficiency of the proposed model
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