Infinitely Many Specific Multicomposite Deterministic and Fuzzy Trigonometric and Hyperbolic Neural Network Approximations
DOI:
https://doi.org/10.65135/atsf.2026.19Keywords:
Deterministic and fuzzy overactivated trigonometric and hyperbolic neural network approximation, quasi-interpolation operator, precisely defined neural network operatorsAbstract
In this article we study in detail the multicomposite deterministic and fuzzy approximation to the unit by infinitely many multicomposite deterministic and fuzzy neural network operators activated by specific multicomposite sigmoid functions. The derived main results generalize earlier related trigonometric and hyperbolic complex valued ones into the deterministic and fuzzy levels. Here the high order multicomposite deterministic and fuzzy pointwise and uniform convergences with rates to the unit operator are given via multicomposite deterministic and fuzzy Jackson type inequalities, respectively. These involve the corresponding moduli of continuity of the mth order (m>= 0) derivatives and H-fuzzy derivatives of the involved deterministic and fuzzy number valued functions, respectively. The treated operators are of averaged type. The derived results are univariate and multivariate.
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