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An Adaptive Fuzzy Min-Max Neural Network Classifier Based on Principle Component Analysis and Adaptive Genetic Algorithm

机译：基于主成分分析和自适应遗传算法的自适应模糊最小-最大神经网络分类器

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摘要

A novel adaptive fuzzy min-max neural network classifier called AFMN is proposed in this paper. Combined with principle component analysis and adaptive genetic algorithm, this integrated system can serve as a supervised and real-time classification technique. Considering the loophole in the expansion-contraction process of FMNN and GFMN and the overcomplex network architecture of FMCN, AFMN maintains the simple architecture of FMNN for fast learning and testing while rewriting the membership function, the expansion and contraction rules for hyperbox generation to solve the confusion problems in the hyperbox overlap region. Meanwhile, principle component analysis is adopted to finish dataset dimensionality reduction for increasing learning efficiency. After training, the confidence coefficient of each hyperbox is calculated based on the distribution of samples. During classifying procedure, utilizing adaptive genetic algorithm to complete parameter optimization for AFMN can also fasten the entire procedure than traversal method. For conditions where training samples are insufficient, data core weight updating is indispensible to enhance the robustness of classifier and the modified membership function can adjust itself according to the input varieties. The paper demonstrates the performance of AFMN through substantial examples in terms of classification accuracy and operating speed by comparing it with FMNN, GFMN, and FMCN.
机译：提出了一种新颖的自适应模糊最小-最大神经网络分类器AFMN。结合主成分分析和自适应遗传算法，该集成系统可以用作监督和实时分类技术。考虑到FMNN和GFMN的伸缩过程中的漏洞以及FMCN的过于复杂的网络架构，AFMN维护FMNN的简单架构以进行快速学习和测试，同时重写了隶属函数，用于生成超框的伸缩规则以解决超框重叠区域中的混淆问题。同时，采用主成分分析完成数据集降维，提高学习效率。训练后，根据样本的分布计算每个超级框的置信系数。在分类过程中，利用自适应遗传算法完成AFMN的参数优化也比遍历方法更快。对于训练样本不足的情况，数据核心权重更新对于增强分类器的鲁棒性是必不可少的，并且修改后的隶属度函数可以根据输入的品种进行自我调整。本文通过与FMNN，GFMN和FMCN进行比较，通过分类和操作速度方面的大量示例展示了AFMN的性能。

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  • 来源
    《Mathematical Problems in Engineering》 |2012年第11期|483535.1-483535.21|共21页
  • 作者

    Jinhai Liu; Zhibo Yu; Dazhong Ma;

  • 作者单位

    School of Information Science and Engineering, Northeastern University, Shenyang 110004, China;

    School of Information Science and Engineering, Northeastern University, Shenyang 110004, China;

    School of Information Science and Engineering, Northeastern University, Shenyang 110004, China;

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