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Approximation by Kantorovich-type max-min operators and its applications
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NSTL
Elsevier
In this study, we construct Kantorovich variant of max-min kind operators, which are nonlinear. By using these new operators, we obtain some uniform approximation results in N dimension ( N >= 1 ). Then, we estimate the error with the help of Holder continuous functions and modulus of continuity. Furthermore, we give some illustrative applications to verify our theory and also investigate some shape-preserving properties of Kantorovichtype max-min Bernstein operator. Lastly, we examine the image processing implementation of our results via Kantorovich-type max-min Shepard operator.(c) 2022 Elsevier Inc. All rights reserved.
Max-min operatorsKantorovich operatorsRate of approximationShape-preserving propertiesFuzzy logicImage processingWEIGHTED APPROXIMATIONPRODUCT OPERATORSCONVERGENCEOPERATIONS
NSTL
Elsevier
