Fuzzy min–max neural networks: from classification to regression

被引：14
|
作者：
R. Tagliaferri
A. Eleuteri
M. Meneganti
F. Barone
机构：
[1] INFM,
[2] Unita` di Salerno and University of Salerno Soft-computing Lab,undefined
[3] DMI via S. Allende,undefined
[4] 84081 Baronissi (Sa),undefined
[5] Italy E-mail: robtag@unisa.it,undefined
[6] INFN,undefined
[7] sez. di Napoli and University of Naples Dip. di Scienze Fisiche via Cintia,undefined
[8] I-80126,undefined
[9] Naples,undefined
[10] Italy,undefined
[11] Alenia Radar Systems,undefined
[12] Naples,undefined
[13] Italy,undefined
来源：
Soft Computing | 2001年 / 5卷 / 1期
关键词：
Keywords Fuzzy neural networks; Fuzzy min-max hyperboxes; Classification; Regression;
D O I：
10.1007/s005000000067
中图分类号：
学科分类号：
摘要：
 In this paper we show two new learning algorithms for a fuzzy min–max neural network. The top down fuzzy min–max (TDFMM) algorithm modifies the classic Simpson's learning algorithm overcoming its main difficulties: the dependence on the presentation order of the patterns and the poor resolutive adaptation to the characteristics of input space. The top down fuzzy min–max regressor (TDFMMR) algorithm extends our neural network to solve regression problems by using a hybrid fuzzy classifier and a gradient descent algorithm.
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页码：69 / 76
页数：7
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