Even in the absence of external stimuli, the brain is spontaneously active. Indeed, most
cortical activity is internally generated by recurrence. Both theoretical and experimental …

This paper investigates the challenging domain of stock market prediction, a significant
aspect of financial markets. It focuses on develo** predictive models to forecast stock …

The work is devoted to the development of the radial basis functions (RBF networks) neural
network new architecture–a polymorphic RBF network in which the one-dimensional radial …

This paper may be the first meta-analysis that presents a comprehensive synthesis of
scientific works spanning the last five years, focusing on methodologies and results related …

Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving
partial differential equations (PDEs) in various scientific and engineering applications …

Corrosion is one of the most significant challenges for oil pipelines. It can occur due to
various factors such as moisture, oxygen, and contaminants in the oil. Corrosion weakens …

This work addresses the cluster synchronization for discrete-time neural networks over
energy-constrained communication channels subjecting to packet loss. A new intermittent …

This research introduces a class of non-parametric identifiers based on differential neural
networks represented by Hamiltonian dynamics. The structure of the identifier corresponds …

Variable-order differential operators can be useful for modeling chaotical systems and
nonlinear fractional differential equations. In this work, we report on a study of the fractional …

This paper investigates the global exponential stability of nonlinear time-delay system
(NTDS) under impulsive control with distributed delay. By establishing a connection …