Application of the Intuitionistic Fuzzy InterCriteria Analysis Method with Triples to a Neural Network Preprocessing Procedure

Sotirov, Sotir; Atanassova, Vassia; Sotirova, Evdokia; Doukovska, Lyubka; Bureva, Veselina; Mavrov, Deyan; Tomov, Jivko

doi:10.1155/2017/2157852

Cited by 27 publications

(8 citation statements)

References 17 publications

(14 reference statements)

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“…In this paper, we introduce a hybrid combination between the intercriteria analysis (ICA, see [13][14][15][16][17][18]) method and modular neural network models. The ICA employs the apparatus of the intuitionistic fuzzy sets (IFS) for detecting possible correlations between pairs of criteria.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach for Modular Neural Network Design Using Intercriteria Analysis and Intuitionistic Fuzzy Logic

et al. 2018

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Intercriteria analysis (ICA) is a new method, which is based on the concepts of index matrices and intuitionistic fuzzy sets, aiming at detection of possible correlations between pairs of criteria, expressed as coefficients of the positive and negative consonance between each pair of criteria. Here, the proposed method is applied to study the behavior of one type of neural networks, the modular neural networks (MNN), that combine several simple neural models for simplifying a solution to a complex problem. They are a tool that can be used for object recognition and identification. Usually the inputs of the MNN can be fed with independent data. However, there are certain limits when we may use MNN, and the number of the neurons is one of the major parameters during the implementation of the MNN. On the other hand, a high number of neurons can slow down the learning process, which is not desired. In this paper, we propose a method for removing part of the inputs and, hence, the neurons, which in addition leads to a decrease of the error between the desired goal value and the real value obtained on the output of the MNN. In the research work reported here the authors have applied the ICA method to the data from real datasets with measurements of crude oil probes, glass, and iris plant. The method can also be used to assess the independence of data with good results.

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Section: Introductionmentioning

confidence: 99%

A Hybrid Approach for Modular Neural Network Design Using Intercriteria Analysis and Intuitionistic Fuzzy Logic

et al. 2018

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“…The theoretical investigations for ICA are published in [2,11,13,21]. The series of papers present the applications of the ICA in the different fields of science: universities rankings [12,14], neural networks preprocessing procedure [19], genetic algorithms [1], economic investigations [8][9][10], medical investigations [22], chemistry applications [20] and etc. In our work for applying ICA to HRST data, we have been interested to detect the eventual correlations between the education inflow data in 35 countries.…”

Section: Application Of Intercriteria Analysis Over Human Resources In Science and Technologymentioning

confidence: 99%

InterCriteria Analysis applied to human resources in science and technology

Rusev¹,

Bureva²

2019

NIFS

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“…The output of an FL model depends on the model type, i.e., Mamdani or Sugeno. Mamdani FL has its output(s) partitioned to memberships with shapes [45, 46]. On the other hand, in Sugeno models (aka Takagi-Sugeno-Kang model), the output is represented as a linear equation or constant.…”

Section: Introductionmentioning

confidence: 99%

Software Development Effort Estimation Using Regression Fuzzy Models

Nassif

Azzeh

Idri

et al. 2019

Computational Intelligence and Neuroscience

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Software effort estimation plays a critical role in project management. Erroneous results may lead to overestimating or underestimating effort, which can have catastrophic consequences on project resources. Machine-learning techniques are increasingly popular in the field. Fuzzy logic models, in particular, are widely used to deal with imprecise and inaccurate data. The main goal of this research was to design and compare three different fuzzy logic models for predicting software estimation effort: Mamdani, Sugeno with constant output, and Sugeno with linear output. To assist in the design of the fuzzy logic models, we conducted regression analysis, an approach we call “regression fuzzy logic.” State-of-the-art and unbiased performance evaluation criteria such as standardized accuracy, effect size, and mean balanced relative error were used to evaluate the models, as well as statistical tests. Models were trained and tested using industrial projects from the International Software Benchmarking Standards Group (ISBSG) dataset. Results showed that data heteroscedasticity affected model performance. Fuzzy logic models were found to be very sensitive to outliers. We concluded that when regression analysis was used to design the model, the Sugeno fuzzy inference system with linear output outperformed the other models.

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Application of the Intuitionistic Fuzzy InterCriteria Analysis Method with Triples to a Neural Network Preprocessing Procedure

Cited by 27 publications

References 17 publications

A Hybrid Approach for Modular Neural Network Design Using Intercriteria Analysis and Intuitionistic Fuzzy Logic

A Hybrid Approach for Modular Neural Network Design Using Intercriteria Analysis and Intuitionistic Fuzzy Logic

InterCriteria Analysis applied to human resources in science and technology

Software Development Effort Estimation Using Regression Fuzzy Models

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