Results 51 to 60 of about 4,379,261 (288)
ENSO dynamics in current climate models: an investigation using nonlinear dimensionality reduction [PDF]
Nonlinear Processes in Geophysics, 2008 Linear dimensionality reduction techniques, notably principal component analysis, are widely used in climate data analysis as a means to aid in the interpretation of datasets of high dimensionality.
I. Ross, P. J. Valdes, S. Wiggins
doaj
Observer‐Based Adaptive Event‐Triggered Tracking Control for Fuzzy TS Systems With Premise Mismatch
International Journal of Adaptive Control and Signal Processing, EarlyView.This paper presents an adaptive logistic event‐triggered observer‐based tracking controller for Takagi‐Sugeno fuzzy systems under constrained inputs and network delays. Leveraging a hybrid LMI and Secretary Bird Optimization approach, this strategy significantly minimizes communication overhead and computational burden while ensuring optimal reference ...
Oussama Djadane +3 more
wiley +1 more source
Fast Tube-Based Robust Compensation Control for Fixed-Wing UAVs
Drones, 2023 When considering the robust control of fixed-wing Unmanned Aerial Vehicles (UAVs), a conflict often arises between addressing nonlinearity and meeting fast-solving requirements.
Lixin Wang +5 more
doaj +1 more source
Dimensionality Reduction with Nvidia Tensor Cores [PDF]
, 2022 openThanks to the popularity and effectiveness of machine learning, the computational requirements for its development have increased beyond the limits of conventional devices.
BALZAN, PIETRO
core
dynoGP: Deep Gaussian Processes for Dynamic System Identification
International Journal of Adaptive Control and Signal Processing, EarlyView.This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli +3 more
wiley +1 more source
nPCA: a linear dimensionality reduction method using a multilayer perceptron
Frontiers in GeneticsBackground: Linear dimensionality reduction techniques are widely used in many applications. The goal of dimensionality reduction is to eliminate the noise of data and extract the main features of data.
Juzeng Li, Yi Wang, Yi Wang
doaj +1 more source
Advanced Engineering Materials, EarlyView.Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt +8 more
wiley +1 more source
Parallel t-SNE Applied to Data Visualization in Smart Cities
IEEE Access, 2020 The growth of smart city applications is increasingly around the world, many cities invest in the development of these systems intending to improve the management and life of their residents.
Maximiliano Araujo Da Silva Lopes +2 more
doaj +1 more source
Advanced Engineering Materials, EarlyView.A numerical–experimental framework is developed for characterizing multi‐matrix fiber‐reinforced polymers (MM‐FRPs) combining epoxy and polyurethane matrices. Harmonic bending tests are integrated with finite element model updating (FEMU) to simultaneously identify elastic and viscoelastic material parameters.
Rodrigo M. Dartora +4 more
wiley +1 more source
Improving reduced-order models through nonlinear decoding of projection-dependent outputs
Patterns, 2023 Summary: A fundamental hindrance to building data-driven reduced-order models (ROMs) is the poor topological quality of a low-dimensional data projection.
Kamila Zdybał +2 more
doaj +1 more source