Is taking natural log superior to not? - Using a characteristics oriented fuzzy Hopfield neural network to identify probability density functions

被引：2

作者：

Yen, Eva C. ^{[1
]}

机构：

[1] Natl Cent Univ, Dept Business Adm, Jhongli 320, Taoyuan County, Taiwan

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2009年 / 36卷 / 03期

关键词：

Characteristics oriented fuzzy rules; Hopfield neural network; Probability density function; Taking natural log or not; CLASSIFICATION;

D O I：

10.1016/j.eswa.2008.06.034

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Lognormal processes are important from a theoretical perspective. We reexamine the problem of whether it is better to take natural log or not? If not, how to identify the probability density function is still an important problem. The assertion that taking natural log is closer to normality is not supported by the simulation and empirical data. The probabilistic neural network contains the entire set of training cases, and is therefore space-consuming and slow to execute. In addition, there is an inverse problem in PNNs, i.e.. we may obtain the same sum of square errors from different density functions. We therefore propose a screening mechanism based on characteristics oriented fuzzy rules in the Hopfield neural network to simplify the estimation process and avoid the inverse problem. From the characteristics oriented fuzzy HNN, we obtain that the best fitting of the data is the Weibull distribution. (C) 2008 Elsevier Ltd. All rights reserved.

引用

页码：5094 / 5099

页数：6

共 22 条

[1] A neural network face recognition system [J].

Aitkenhead, MJ ;

McDonald, AJS .

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2003, 16 (03) :167-176

[2] On the relationship between deterministic and probabilistic directed Graphical models: From Bayesian networks to recursive neural networks [J].

Baldi, P ;

Rosen-Zvi, M .

NEURAL NETWORKS, 2005, 18 (08) :1080-1086

[3] MODELLING SMALL-BUSINESS CREDIT SCORING BY USING LOGISTIC REGRESSION, NEURAL NETWORKS AND DECISION TREES [J].

Bensic, Mirta ;

Sarlija, Natasa ;

Zekic-Susac, Marijana .

INTELLIGENT SYSTEMS IN ACCOUNTING FINANCE & MANAGEMENT, 2005, 13 (03) :133-150

[4] Constructive training of probabilistic neural networks [J].

Berthold, MR ;

Diamond, J .

NEUROCOMPUTING, 1998, 19 (1-3) :167-183

[5] THE CAPACITY OF THE KANERVA ASSOCIATIVE MEMORY [J].

CHOU, PA .

IEEE TRANSACTIONS ON INFORMATION THEORY, 1989, 35 (02) :281-298

[6] A comparison of selected artificial neural networks that help auditors evaluate client financial viability [J].

Etheridge, HL ;

Sriram, RS ;

Hsu, HYK .

DECISION SCIENCES, 2000, 31 (02) :531-550

[7] Towards probabilistic footy tipping: a hybrid approach utilising genetically defined neural networks and linear programming [J].

Flitman, AM .

COMPUTERS & OPERATIONS RESEARCH, 2006, 33 (07) :2003-2022

[8] Probabilistic neural networks for the identification of qualified audit opinions [J].

Gaganis, Chrysovalantis ;

Pasiouras, Fotios ;

Doumpos, Michael .

EXPERT SYSTEMS WITH APPLICATIONS, 2007, 32 (01) :114-124

[9] Implementing wavelet/probabilistic neural networks for Doppler ultrasound blood flow signals [J].

Guler, Inan ;

Ubeyli, Elif Derya .

EXPERT SYSTEMS WITH APPLICATIONS, 2007, 33 (01) :162-170

[10]

HO TK, 1994, IEEE T PATTERN ANAL, V16, P66, DOI 10.1109/34.273716

← 1 2 3 →