基于期望值简约的模糊非线性回归

doi:10.3969/j.issn.1000-1565.2018.01.014

河北大学学报(自然科学版) ›› 2018, Vol. 38 ›› Issue (1): 89-98.DOI: 10.3969/j.issn.1000-1565.2018.01.014

基于期望值简约的模糊非线性回归

王熙照¹,赵士欣^1,2

收稿日期:2017-09-07 出版日期:2018-01-25 发布日期:2018-01-25
通讯作者: 赵士欣(1978—),女,河北石家庄人,河北大学博士研究生,主要从事机器学习与不确定性信息处理研究.E-mail:cssxzhao@163.com
作者简介:王熙照(1963—),男,河北保定人,河北大学教授,博士生导师,主要从事机器学习与不确定性信息处理研究. E-mail:xizhaowang@ieee.org
基金资助:
国家自然科学基金资助项目(71371063);河北省自然科学基金资助项目(A2015210103);河北省教育厅青年基金资助项目(QN2016140)

Expectation reduction-based fuzzy nonlinear regression

WANG Xizhao¹, ZHAO Shixin^1,2

1.College of Management, Hebei University, Baoding 071002, China
2.Department of Mathematics and Physics, Shijiazhuang Tiedao University, Shijiazhuang 050043, China

Received:2017-09-07 Online:2018-01-25 Published:2018-01-25

摘要/Abstract

摘要： 为解决不确定性数据的学习问题,借鉴2-型模糊理论中的简约思想,提出了一种利用期望值简约技术处理三角型模糊输入输出数据的模糊非线性回归模型.首先将三角型模糊输入输出利用其期望值简约为清晰输入输出,然后利用经典随机赋权网络对其进行学习,最后再通过新定义的三角型模糊输出变量的宽度矩阵将网络清晰值输出还原为三角型模糊输出.仿真实验结果表明,与已有模型相比,该模型具有更高的学习准确度和更好的扩展能力.

关键词: 期望值简约, 三角型模糊数, 随机赋权网络, 模糊非线性回归

Abstract: Inspired by the idea of reduction in 2-fuzzy theory, an expectation reduction-based fuzzy nonlinear regression model is proposed to deal with triangular fuzzy number inputs and outputs.In this model the triangular fuzzy inputs and outputs are firstly replaced by their expectations, then the data can be trained with classical random weight network.Finally the crisp real outputs are recover to triangular fuzzy outputs by using a width matrix of original target triangular fuzzy outputs.The experiment results show that the proposed model gives higher learning accuracy and better generalization.

Key words: expectation reduction, triangular fuzzy number, random weight network, fuzzy nonlinear regression

中图分类号:

TP183

王熙照,赵士欣. 基于期望值简约的模糊非线性回归[J]. 河北大学学报(自然科学版), 2018, 38(1): 89-98.

WANG Xizhao, ZHAO Shixin. Expectation reduction-based fuzzy nonlinear regression[J]. Journal of Hebei University (Natural Science Edition), 2018, 38(1): 89-98.

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基于期望值简约的模糊非线性回归

Expectation reduction-based fuzzy nonlinear regression

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