Abstract: This paper studies the distributed optimization problem of high-order multi-agent systems with unknown nonlinear terms and input saturation. Unlike existing results, nonlinear functions in ...
It appears that no particular approximate [nonlinear] filter is consistently better than any other, though . . . any nonlinear filter is better than a strictly linear one. 1 The Kalman filter is a ...
Activation functions play a critical role in AI inference, helping to ferret out nonlinear behaviors in AI models. This makes them an integral part of any neural network, but nonlinear functions can ...
In this work, we adopt a new approach to the construction of a global theory of algebras of generalized functions on manifolds based on the concept of smoothing operators. This produces a ...
Abstract: This paper investigates global adaptive asymptotic tracking with prescribed performance control (PPC) for nonlinear systems subject to unknown nonparametric uncertainties, mismatched ...
Researchers from Massachusetts Institute of Technology (MIT), Enosemi, and Periplous developed a fully integrated photonic processor that can perform all the key computations of a deep neural network ...
Derivative-free method to find zeros of analytic (holomorphic) functions / solve nonlinear (polynomial / generalized) eigenvalue problems using contour integration. (Block SS-Hankel method, Block ...
Results that may be inaccessible to you are currently showing.
Hide inaccessible results