Multivariate fuzzy neural network interpolation operators and applications to image processing

Kadak U.

EXPERT SYSTEMS WITH APPLICATIONS, vol.206, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 206
  • Publication Date: 2022
  • Doi Number: 10.1016/j.eswa.2022.117771
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Computer & Applied Sciences, INSPEC, Metadex, Public Affairs Index, Civil Engineering Abstracts
  • Keywords: Fuzzy interpolation operators, Neural network interpolation, Fuzzy image interpolation, Deep learning, Machine learning
  • Gazi University Affiliated: Yes


In this paper, we introduce a novel family of multivariate fuzzy neural network interpolation operators activated by sigmoidal functions belonging to the new class of multivariate sigmoidal functions. To present an alternative way to the well-known shortcomings of the Hukuhara difference, we use a proper function defined on a set of fuzzy..-cell numbers. Moreover, we construct the Kantorovich variant of fuzzy NN interpolation operators, and also achieve approximation properties via L-p-type metric with respect to both modulus of continuity and L-p-modulus of continuity in fuzzy sense. Various special examples for the class of multivariate sigmoidal functions are presented. Also, we give some illustrative examples to demonstrate the approximation performances of all the above operators. Finally, we give a novel interpolation algorithm involving a multidimensional fuzzy inference system with applications in color image resizing and inpainting.