Recent advancements in neural network design have given rise to the development of
Kolmogorov-Arnold Networks (KANs), which enhance interpretability and precision of these …

This work delves into the passivity of fractional generalized Cohen–Grossberg neural
networks (CGNNs) with proportional delays. In particular, two forms of Lipschitz conditions …

The neural network methods in solving differential equations have significant research
importance and promising application prospects. Aimed at the time-fractional Huxley (TFH) …

Artificial neural network (ANN) have shown great success in various scientific fields over
several decades. Recently, one of its variants known as deep feedforward neural network …

A new physics-informed neural network (PINN) algorithm is proposed to solve variable-order
space-fractional partial differential equations (PDEs). For the forward problem, PINN …

The issues of finite time passivity are explored for BAM reaction–diffusion neural networks
including discrete delayed and Caputo fractional partial differential operator. With the help of …

The efficacy of advanced deep-learning diagnostic methods is contingent mainly upon
sufficient trainable data for each fault category. However, gathering ample data in real-world …

Rail transit has many advantages, such as large passenger capacity, convenience, safety,
and environmental protection, making it the preferred travel mode for most passengers …

There are many algorithms based on orthogonal functions that can be applied to real-world
problems. For example, many of them can be reduced to approximate the solution of a …

In recent years, machine learning has become an interdisciplinary research hotspot in
nonlinear science and artificial intelligence. Nonlinear integro-differential equations (IDEs) …