Function approximation, a central theme in numerical analysis and applied mathematics, seeks to represent complex functions through simpler or more computationally tractable forms. In this context, ...
A new technical paper titled “Massively parallel and universal approximation of nonlinear functions using diffractive ...
Notifications You must be signed in to change notification settings This project explores how a Deep Neural Network (DNN) can approximate a 4th-degree polynomial function using simulated data. The ...
In this project, I used Multi-Layer Perceptrons (MLPs) to approximate a few one-dimensional functions. The idea was to start with something simple (like a straight line) and move toward more complex ...
Two near minimax norms for polynomial approximation are presented. They are designed for approximation of both a function and its first derivative uniformly by polynomials over a given finite interval ...
Abstract: Control Lyapunov functions are traditionally used to design a controller which ensures convergence to a desired state, yet deriving these functions for nonlinear systems remains a complex ...
UCLA researchers demonstrate diffractive optical processors as universal nonlinear function approximators using linear ...
Results that may be inaccessible to you are currently showing.
Hide inaccessible results
Feedback