We study gradient flow on the multi-index regression problem for high-dimensional
Gaussian data. Multi-index functions consist of a composition of an unknown low-rank linear …

The aim of this paper is to investigate the error which results from the method of
approximation operators with logarithmic sigmoidal function. By means of the method of …

Here we study the multivariate quantitative constructive approximation of real and complex
valued continuous multivariate functions on a box or RN, N∈ N, by the multivariate quasi …

Here we give the univariate quantitative approximation of real and complex valued
continuous functions on a compact interval or all the real line by quasi-interpolation …

We construct neural network interpolation operators with some newly defined activation
functions, and give the approximation rate by the operators for continuous functions. By …

In this paper, we introduce a new type of interpolation operators by using Lagrange
polynomials of degree r, which can be regarded as feedforward neural networks with four …

This paper shows that large nonparametric classes of conditional multivariate densities can
be approximated in the Kullback–Leibler distance by different specifications of finite mixtures …

Adaptive optimization (AO) schemes based on stochastic approximation principles such as
the Random Directions Kiefer–Wolfowitz (RDKW), the Simultaneous Perturbation Stochastic …

Despite the continuous advances in the fields of intelligent control and computing, the
design and deployment of efficient large scale nonlinear control systems (LNCSs) requires a …

In this paper, we study the problem of optimizing (fine-tuning) the design parameters of large-
scale traffic control systems that are composed of distinct and mutually interacting modules …