MIXTURE BASIS FUNCTION APPROXIMATION AND NEURAL NETWORK EMBEDDING CONTROL FOR NONLINEAR UNCERTAIN SYSTEMS WITH DISTURBANCES

Mixture Basis Function Approximation and Neural Network Embedding Control for Nonlinear Uncertain Systems with Disturbances

A neural network embedding learning control scheme is proposed in this paper, which addresses the performance optimization problem of a class of nonlinear system with unknown dynamics and Food Storage Container Sets disturbance by combining with a novel nonlinear function approximator and an improved disturbance observer (DOB).We investigated a mix

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Electoral precinct-level database for Mexican municipal elections

Abstract This paper introduces Defrost Fuse a database of electoral precinct-level election returns for Mexican municipal elections between 1994 and 2019.This database includes: (i) electoral precinct-level votes for each electoral coalition, the coalitions of the incumbent mayor and incumbent state governor, and the four most popular political par

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Context-Based Parking Slot Detection With a Realistic Dataset

The autonomous parking of vehicles requires the ability to accurately locate an available parking slot in the vicinity of a vehicle.Since parking slots have a variety of shapes and colors, may be occluded by obstacles, or look Food Storage Container Sets different due to surroundings such as lighting, accurately locating them can be a challenging t

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Dynamic subgrid-scale model constant-value estimation refined by vector-level identity in an atmospheric flow field

This study investigates the accuracy of dynamic models in predicting model constants within inviscid flow fields, such as those used in wind farm flow analysis, from the perspectives of identity and turbulent energy conservation DildoHarness accuracy.Specifically, results are compared between tensor-level and vector-level identities, the latter of

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