论进化神经模糊系统的协同作用

Vivek Srivastava, B. Tripathi, V. Pathak, Anand Tiwari
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引用次数: 1

摘要

近年来,人们已经看到进化、模糊和神经网络的协同作用由于其综合计算效率而越来越受欢迎。本文研究了三种不同效度准则下进化模糊聚类与神经网络协同作用的可行性。我们还报道了各种参数如模糊化、离群控制和泛化参数对系统性能的影响。本文将进化模糊聚类的所有变体用于神经网络的结构选择和学习。在广泛的基准问题和生物特征识别问题上进行了性能评估。实验结果对进化模糊聚类与神经网络的协同作用进行了对比分析。研究发现,基于谢贝尼准则和神经网络的进化模糊聚类优于其他聚类。同时,进化模糊聚类与神经网络相结合的聚类效果远优于模糊聚类与神经网络的协同效果。我们甚至在包括眼动、面部和眼周生物特征在内的生物特征数据集上也获得了令人鼓舞的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the synergism of evolutionary neuro-fuzzy system
In the recent past, it has been seen that the synergism of evolutionary, fuzzy and neural network is gaining popularity over individual techniques due to its combined computational efficiency. In this paper, we have investigated the feasibility of synergism between evolutionary fuzzy clustering using three different validity criteria and neural networks. We have also reported the effect of various parameters such as fuzzifier, outlier control and generalization parameter over system performance. Here, all the variants of evolutionary fuzzy clustering are employed for structure selection and learning of neural network. Performance evaluation has been carried out over wide spectrum of benchmark problems and biometric recognition problems. Experimental results demonstrate the comparative analysis of synergism of evolutionary fuzzy clustering with neural network. It has been found that evolutionary fuzzy clustering using Xie Beni criteria with neural network outperforms over other variants. Also, evolutionary fuzzy clustering combined with neural network performs far better than the synergism of fuzzy clustering with neural network. We have obtained promising results even for biometric datasets including eye-movement, face and periocular biometrics.
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