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 ...
UCLA researchers demonstrate diffractive optical processors as universal nonlinear function approximators using linear ...
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 ...
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Optical system uses diffractive processors to achieve large-scale nonlinear computation
Researchers at the University of California, Los Angeles (UCLA) have developed an optical computing framework that performs ...
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