Trigonometric and Hyperbolic Generated Approximation Theory

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The book explores the innovative impact of new trigonometric and hyperbolic Taylor's formulas with integral remainders, offering a comprehensive collection of approximations. It delves into perturbed neural network approximations, their links to Brownian motion, and various analytical inequalities. Covering both univariate and multivariate cases, it addresses Korovkin theory and singular integrals. The findings have broad applications across mathematics, computer science, engineering, and artificial intelligence, making it a vital resource for researchers and students in related fields.