Displaying similar documents to “Robust estimations in classical regression models versus robust estimations in fuzzy regression models”

The estimation of electric power losses in electrical networks by fuzzy regression model using genetic algorithm.

A. V. Mogilenko, D. A. Pavlyuchenko (2004)

Mathware and Soft Computing

Similarity:

This paper presents the comparative study for fuzzy regression model using linear programming, fuzzy regression model using genetic algorithms and standard regression model. The fuzzy and standard models were developed for estimation of electric power losses in electrical networks. Simulation was carried out with a tool developed in MATLAB.

A theoretical comparison of disco and CADIAG-II-like systems for medical diagnoses

Tatiana Kiseliova (2006)

Kybernetika

Similarity:

In this paper a fuzzy relation-based framework is shown to be suitable to describe not only knowledge-based medical systems, explicitly using fuzzy approaches, but other ways of knowledge representation and processing. A particular example, the practically tested medical expert system Disco, is investigated from this point of view. The system is described in the fuzzy relation-based framework and compared with CADIAG-II-like systems that are a “pattern” for computer-assisted diagnosis...

Disjointness of fuzzy coalitions

Milan Mareš, Milan Vlach (2008)

Kybernetika

Similarity:

The cooperative games with fuzzy coalitions in which some players act in a coalition only with a fraction of their total “power” (endeavor, investments, material, etc.) or in which they can distribute their “power” in more coalitions, are connected with some formal or interpretational problems. Some of these problems can be avoided if we interpret each fuzzy coalition as a fuzzy class of crisp coalitions, as shown by Mareš and Vlach in [9,10,11]. The relation between this model of fuzziness...

Stock price forecasting: Autoregressive modelling and fuzzy neural network.

Dusan Marcek (2000)

Mathware and Soft Computing

Similarity:

Most models for the time series of stock prices have centered on autoregresive (AR) processes. Traditionaly, fundamental Box-Jenkins analysis [3] have been the mainstream methodology used to develop time series models. Next, we briefly describe the develop a classical AR model for stock price forecasting. Then a fuzzy regression model is then introduced. Following this description, an artificial fuzzy neural network based on B-spline member ship function is presented as an alternative...