2015
DOI: 10.1186/s13663-015-0343-0
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Some generalized fixed point results in a b-metric space and application to matrix equations

Abstract: We have proved a generalized Presic-Hardy-Rogers contraction principle and Ciric-Presic type contraction principle for two mappings in a b-metric space. As an application, we derive some convergence results for a class of nonlinear matrix equations. Numerical experiments are also presented to illustrate the convergence algorithms.MSC: coincident point; common fixed point; b-metric space; matrix equation

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Cited by 15 publications
(10 citation statements)
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“…Rough set theory (RST) [5,6] which is one of the successful approximations based mathematical model to deal the imprecision and uncertainty present in knowledge. Many heuristic algorithms are proposed based on rough set theory, also numerous approached based on rough set theory and other theories are investigated to extract decision rules and reduce the dimensionality of dataset [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26].One advantage of the rough set is the creation of readable if-then rules. Such rules have a potential to reveal new patterns in the data material.…”
Section: Introductionmentioning
confidence: 99%
“…Rough set theory (RST) [5,6] which is one of the successful approximations based mathematical model to deal the imprecision and uncertainty present in knowledge. Many heuristic algorithms are proposed based on rough set theory, also numerous approached based on rough set theory and other theories are investigated to extract decision rules and reduce the dimensionality of dataset [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26].One advantage of the rough set is the creation of readable if-then rules. Such rules have a potential to reveal new patterns in the data material.…”
Section: Introductionmentioning
confidence: 99%
“…Shaaban et al [41,42] used rough set methodology for steam turbine-generator fault diagnosis and for water reservoirs site location decision making, respectively. For more applications see [43][44][45][46][47][48][49][50][51][52][53][54]. One advantage of the rough set is the creation of readable if-then rules.…”
Section: Introductionmentioning
confidence: 99%
“…In the fault classification step, the selected features are used to train artificial intelligence techniques like k-nearest neighbor (kNN), artificial neural networks and support vector machine (SVM), fuzzy sets [8][9][10], rough set theory (RST) [11,12] which is one of the successful approximation based mathematical model to deal the imprecision and uncertainty present in knowledge. Many heuristic algorithms are proposed based on rough set theory, also numerous approached based on rough set theory and other theories are investigated to extract decision rules and reduce the dimensionality of dataset [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]…”
Section: Introductionmentioning
confidence: 99%