To solve the time-variant Sylvester equation, in 2013, Li et al. proposed the zeroing neural network with sign-bi-power function (ZNN-SBPF) model via constructing a nonlinear activation function. In ...

Neuroscientists want to understand how individual neurons encode information that allows us to distinguish objects, like ...

Researchers have long been interested in how humans and animals make decisions by focusing on trial-and-error behavior ...

The strength of certain neural connections can predict how well someone can learn math, and mildly electrically stimulating ...

Using machine learning and math, a Brigham Young University student improved a key tool firefighters rely on during wildfire ...

Brigham Young University graduate Jane Housley's research could help make a widely used wildfire modeling tool faster and ...

This letter investigates the universal approximation capabilities of Hamiltonian Deep Neural Networks (HDNNs) that arise from the discretization of Hamiltonian Neural Ordinary Differential Equations.

Using machine learning and math, a BYU student improved a key tool firefighters rely on during wildfire season ...

This important study demonstrates the significance of incorporating biological constraints in training neural networks to develop models that make accurate predictions under novel conditions. By ...

This repository is the open source code for my latest work: "Through-the-Wall Radar Human Activity Recognition WITHOUT Using Neural Networks", submitted to arXiv. Fig. 1. Current works in this field ...