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Adaptive Neuro-Fuzzy Inference System: Overview, Strengths, Limitations, and Solutions

机译:自适应神经模糊推理系统:概述,优势,局限和解决方案

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摘要

Adaptive neuro-fuzzy inference system (ANFIS) is efficient estimation model not only among neuro-fuzzy systems but also var-ious other machine learning techniques. Despite acceptance among researchers, ANFIS suffers from limitations that halt applications in problems with large inputs; such as, curse of dimensionality and computational expense. Various approaches have been proposed in literature to overcome such shortcomings, however, there exists a considerable room of improvement. This paper reports approaches from literature that reduce computational complexity by architectural modifications as well as efficient training procedures. Moreover, as potential future directions, this paper also proposes conceptual solutions to the limitations highlighted.
机译:自适应神经模糊推理系统(ANFIS)不仅是神经模糊系统中的有效估计模型,还是各种其他机器学习技术的有效估计模型。尽管获得了研究人员的认可,但ANFIS仍存在一些局限性,无法解决大量输入问题中的应用。例如维数和计算费用的诅咒。在文献中已经提出了各种方法来克服这些缺点,但是,存在很大的改进空间。本文报道了一些文献的方法,这些方法通过架构修改以及有效的培训程序来降低计算复杂性。此外,作为潜在的未来方向,本文还针对突出显示的局限性提出了概念性解决方案。

著录项

  • 来源
    《Data Mining and Big Data》|2017年|527-535|共9页
  • 会议地点 Fukuoka(JP)
  • 作者单位

    Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia. Batu Pahat, Johor, Malaysia;

    Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia. Batu Pahat, Johor, Malaysia;

    Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia. Batu Pahat, Johor, Malaysia;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    ANFIS; Fuzzy logic; Neural network; Neuro-fuzzy; Big data;

    机译:ANFIS;模糊逻辑;神经网络;神经模糊大数据;

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