IEEE

A Fast Closed-Form Gradient-Descent Algorithm for Beampattern Synthesis

Abstract: A fast gradient-descent (FGD) method is proposed for far-field pattern synthesis of large antenna arrays. Compared with conventional gradient-descent (GD) methods for pattern synthesis where ...
Hosted on MSN

Linear regression using gradient descent explained simply

Understand what is Linear Regression Gradient Descent in Machine Learning and how it is used. Linear Regression Gradient Descent is an algorithm we use to minimize the cost function value, so as to ...
The New Yorker

The Case That A.I. Is Thinking

Dario Amodei, the C.E.O. of the artificial-intelligence company Anthropic, has been predicting that an A.I. “smarter than a Nobel Prize winner” in such fields as biology, math, engineering, and ...
Scientific Research Publishing

Deift, P. and Zhou, X. (1993) A Steepest Descent Method for Oscillatory Riemann-Hilbert Problems. Asymptotics for the Mkdv Equation.

ABSTRACT: In this paper, we primarily investigate several exact solutions of the (2 + 1)-dimensional KdV equation and summarize the trajectory equations after collisions between these solutions. Using ...
IEEE

Gradient Descent Algorithm Based on Extended State Observer for Loss Minimization of Ultralocal Model Predictive Controlled PMSM Drives

Abstract: Hybrid loss minimization algorithms in electrical drives combine the benefits of search-based and model-based approaches to deliver fast and robust dynamic responses. This article presents a ...
Visual Studio Magazine

Kernel Ridge Regression with Stochastic Gradient Descent Training Using C#

Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses stochastic gradient descent, one of two ...