Number 1. May 1999

Business failure prediction using rough sets: a comparison with multivariate analysis techniques

C. Zopounidis, M. Doumpos. Technical University of Crete

R. Slowinski, R. Susmaga. Poznan University of Technology

A.I. Dimitras. University of Crete

Fuzzy Economic Review: Volume IV, Number 1. May 1999
DOI: 10.25102/fer.1999.01.01

Several multivariate analysis techniques have been used in the past for the prediction of business failure. Although some of these methods lead to models with a satisfactory ability to discriminate between healthy and bankrupt firms, they suffer from some limitations, often due to the unrealistic assumption of statistical hypotheses or…

The performance requirements analysis with fuzzy logic

G. Zollo, L. Iandoli. University of Naples

A. Cannavacciuolo. Fiat Research Center

Fuzzy Economic Review: Volume IV, Number 1. May 1999
DOI: 10.25102/fer.1999.01.02

This paper presents an innovative approach based on fuzzy logic to the Performance Requirements Analysis which is the first step of Target Costing. This analysis provides relevant information to designers on the importance of functions and components of the product. Traditional models are not able to process qualitative information provided…

Intuitionistic fuzzy interpretation of information messages

K. Atanassov. Bulgarian Academy of Sciences

D. Dimitrov. University of National and World Economy

Fuzzy Economic Review: Volume IV, Number 1. May 1999
DOI: 10.25102/fer.1999.01.03

The information exchange takes a very important role in several areas of the economic theory. The main question about this process is how the information received can be estimated. In the paper we analyze this exchange of information between a set of informed players and an uninformed player by using…

Similarities in fuzzy regression models and application on transportation

V. A. Profillidis, B. K. Papadopoulos, G. N. Botzoris. Democritus University of Thrace

Fuzzy Economic Review: Volume IV, Number 1. May 1999
DOI: 10.25102/fer.1999.01.04

In [1] and [2] we have studied the set of the solutions of a fuzzy regression model that comes from a set of data, as a metric space with appropriate metrics on it. Using the similarity ratio which has been found in [1] and [2] we compare models that come…

Volume IV, Number 1. May 1999

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