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Issue:Opportunities for application of the intercriteria analysis method to neural network preprocessing procedures: Difference between revisions

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  | file            = NIFS-21-4-143-152.pdf
  | file            = NIFS-21-4-143-152.pdf
  | format          = PDF
  | format          = PDF
  | size            = 99
  | size            = 275
  | abstract        = The artificial neural networks (ANN) are a tool that can be used for object recognition and identification. However, there are certain limits when we may use ANN, and the number of the neurons is one of the major parameters during the implementation of the ANN. On the other hand, the bigger number of neurons slows down the learning process. In our paper, we use a method for removing the number of the neurons without reducing the error between the target value and the real value obtained at the output of the ANN’s output. The method uses the recently proposed approach of InterCriteria Analysis, based on index matrices and intuitionistic fuzzy sets, which aims to detect possible correlations between pairs of criteria. In this paper we use the data from 11 criteria of crude oil measurements.
  | abstract        = The artificial neural networks (ANN) are a tool that can be used for object recognition and identification. However, there are certain limits when we may use ANN, and the number of the neurons is one of the major parameters during the implementation of the ANN. On the other hand, the bigger number of neurons slows down the learning process. In our paper, we use a method for removing the number of the neurons without reducing the error between the target value and the real value obtained at the output of the ANN’s output. The method uses the recently proposed approach of InterCriteria Analysis, based on index matrices and intuitionistic fuzzy sets, which aims to detect possible correlations between pairs of criteria. In this paper we use the data from 11 criteria of crude oil measurements.
  | keywords        = Intercriteria analysis, Intuitionistic fuzzy sets, Neural networks, Crude oil.
  | keywords        = Intercriteria analysis, Intuitionistic fuzzy sets, Neural networks, Crude oil.

Revision as of 17:35, 19 January 2016

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Title of paper: Opportunities for application of the intercriteria analysis method to neural network preprocessing procedures
Author(s):
Sotir Sotirov
Laboratory of Intelligent Systems, University “Prof. Dr. Assen Zlatarov”, 1 “Prof. Yakimov” Blvd., Burgas 8010, Bulgaria
ssotirov@btu.bg
Published in: "Notes on IFS", Volume 21, 2015, Number 4, pages 143–152
Download:  PDF (275  Kb, File info)
Abstract: The artificial neural networks (ANN) are a tool that can be used for object recognition and identification. However, there are certain limits when we may use ANN, and the number of the neurons is one of the major parameters during the implementation of the ANN. On the other hand, the bigger number of neurons slows down the learning process. In our paper, we use a method for removing the number of the neurons without reducing the error between the target value and the real value obtained at the output of the ANN’s output. The method uses the recently proposed approach of InterCriteria Analysis, based on index matrices and intuitionistic fuzzy sets, which aims to detect possible correlations between pairs of criteria. In this paper we use the data from 11 criteria of crude oil measurements.
Keywords: Intercriteria analysis, Intuitionistic fuzzy sets, Neural networks, Crude oil.
AMS Classification: 03E72.
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