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Fuzzy Radial Basis Function Neural Networks with information granulation and its parallel genetic optimization

机译:信息粒化的模糊径向基函数神经网络及其并行遗传优化

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摘要

Fuzzy modeling of complex systems is a challenging task, which involves important problems of dimensionality reduction and calls for various ways of improving the accuracy of modeling. The IG-FRBFNN, a hybrid architecture of the IG-FIS (Fuzzy Inference System) and FRBFNN (Fuzzy Radial Basis Function Neural Networks), is proposed to address these problems. The paper is concerned with the analysis and design of IG-FRBFNNs and their optimization by means of the Hierarchical Fair Competition-based Parallel Genetic Algorithm (HFC-PGA). In the proposed network, the membership functions of the premise part of the fuzzy rules of the IG-based FRBFNN model directly rely on the computation of the relevant distance between data points and the use of four types of polynomials such as constant, linear, quadratic and modified quadratic are considered for the consequent part of fuzzy rules. Moreover, the weighted Least Square (WLS) learning is exploited to estimate the coefficients of the polynomial forming the conclusion part of the rules. Since the performance of the IG-RBFNN model is affected by some key design parameters, such as a specific subset of input variables, the fuzzification coefficient of the FCM, the number of rules, and the order of polynomial of the consequent part of fuzzy rules, it becomes beneficial to carry out both structural as well as parametric optimization of the network. In this study, the HFC-PGA is used as a comprehensive optimization vehicle. The performance of the proposed model is illustrated by means of several representative numerical examples.
机译:复杂系统的模糊建模是一项艰巨的任务,它涉及降维的重要问题,并要求以各种方式提高建模的准确性。 IG-FRBFNN是IG-FIS(模糊推理系统)和FRBFNN(模糊径向基函数神经网络)的混合架构,旨在解决这些问题。本文研究了基于层次公平竞争的并行遗传算法(HFC-PGA)对IG-FRBFNN的分析和设计及其优化。在所提出的网络中,基于IG的FRBFNN模型的模糊规则的前提部分的隶属函数直接依赖于数据点之间的相关距离的计算以及使用四种类型的多项式,例如常数,线性,二次在模糊规则的后续部分中考虑了二次方和修正二次方。此外,利用加权最小二乘(WLS)学习来估计构成规则结论部分的多项式系数。由于IG-RBFNN模型的性能受一些关键设计参数的影响,例如输入变量的特定子集,FCM的模糊系数,规则数量以及模糊规则后续部分的多项式阶数,对网络进行结构优化和参数优化都是有益的。在这项研究中,HFC-PGA被用作全面的优化工具。通过几个典型的数值示例说明了所提出模型的性能。

著录项

  • 来源
    《Fuzzy sets and systems》 |2014年第16期|96-117|共22页
  • 作者单位

    Department of Electrical Engineering, The University of Suwon, San 2-2 Wan-ri, Bongdam-eup, Hwaseong-si, Gyeonggi-do, 445-743, South Korea;

    Department of Electrical Engineering, The University of Suwon, San 2-2 Wan-ri, Bongdam-eup, Hwaseong-si, Gyeonggi-do, 445-743, South Korea;

    Department of Electrical & Computer Engineering, University of Alberta, Edmonton T6R 2V4 AB, Canada,Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia,Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland;

    Department of Electronic Engineering, Seokyeong University, Jungneung-Dong 16-1, Sungbuk-Gu, Seoul 136-704, South Korea;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Fuzzy radial basis function neural network; Fuzzy C-Means clustering; Hierarchical fair competition parallel genetic algorithm; Weighted least squares method;

    机译:模糊径向基函数神经网络模糊C均值聚类;分层公平竞争并行遗传算法;加权最小二乘法;
  • 入库时间 2022-08-18 02:59:07

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