Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 21 for “"NARX"”.
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Wind regime characterization, and short-term wind speed and power forecasting using multivariable LSTM and NARX networks in the Andes Mountains, Ecuador
En los últimos años, la investigación ha revelado que los diseños hidroeléctricos en Ecuador no han considerado adecuadamente la sensibilidad al cambio climático. Además, las condiciones climáticas determinan las variaciones en la generación de electricidad a partir de esta fuente de energía …
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Forecasting short term trucking rates
… Autoregressive Models with eXogenous input (NARX) models. NARX models are powerful when modelling complex, nonlinear and dynamic systems, especially time series. Traditional time series models, including autoregressive integrated moving average (ARIMA), are also used and results from …
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Wind Speed Forecasting for Power Generation Using a Self-Assembling Closed-Loop Recurrent Neural Network
… against a proven recurrent neural network, NARX (Non-linear Auto-Regressive neural network with eXogenous inputs), on several univariate and multivariate time series, including weather measurements from the Bogdanci Wind Park in Macedonia. Artificial neural networks, such as NARX, require a …
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Independence and interdependence: signal transduction of two chemosensory receptors important for the regulation of gliding motility in Myxococcus xanthus
… protein that is composed of the N-terminus of NarX (nitrate sensor kinase) and the C-terminus of DifA. This NarX-DifA chimera restores the DifA functionality (EPS production, agglutination, S-motility and development) to a "difA mutant in a nitrate-dependent manner, suggesting DifA shares a …
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Realising data-centric UAV autonomy through learning-based prediction and feedback integration
… autoregressive model with exogenous inputs (NARX). Evaluation is horizon-sensitive and employs MSE, RMSE, R squared, and dynamic time warping (DTW) to separate transient and steady-tate behaviour and expose error structure. Under identical dataset splits and preprocessing, a companion LSTM …
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Elaboração de modelo híbrido apto à construção de gêmeos digitais : estudo de caso na produção de metanol por hidrogenação catalítica do CO2 em reator de leito fixo
… Então, foi elaborado um modelo preditivo do tipo NARX (Nonlinear Autoregressive Exogenous Model) utilizando redes neurais artificiais do tipo feedforward como o modelo não linear. A partir do modelo NARX, derivou-se dois modelos: um modelo caixa-preta, que utiliza diretamente as entradas virtuais …
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Biosynthesis of Nucleotide Sugar Monomers for Exopolysaccharide Production in Myxococcus Xanthus
… of the pathway. Previous studies had created a NarX-DifA chimeric protein, NafA, that enables the activation of the Dif pathway by nitrate, the signal for NarX. In this study, we constructed a Δpgi difA double mutant containing NafA. This strain was then subjected to various incubations with …
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Identifying and Predicting Rat Behavior Using Neural Networks
… autoregressive process with exogenous inputs (NARX) neural network can partially identify between different behaviors and can generally determine the velocity and spatial position attributes of the identified behavior inside and outside of the trained interval</p>
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A machine learning model of Manhattan air pollution at high spatial resolution
… public health challenge. A neural network NARX model was created in MATLAB for each cell on a 250m square grid laid over Manhattan, for a total of 907 individual models across the city, for PM2 .5 , CO, NO2 , 03, and SO 2. In addition to standard meteorological inputs, data describing the …
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EEG Feature Extraction and Pattern Recognition Based on Chaotic Systems
… and Chua’s systems are used for the study. A NARX (Nonlinear Autoregressive Exogenous) model is proposed to train bifurcation patterns of chaotic systems and performance of various NARX topologies in modelling the bifurcation patterns is estimated. Previous efforts to model an attractor were …
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Beans to Bytes: Grey-Box Nonlinear System Identification Using Hybrid Physics-Neural Network Models
… as well as illustrating the limits of LSTM, Deep NARX models using "one-step" forward prediction techniques. Although the study focuses explicitly on coffee roasting, the conclusions drawn are applicable to other similarly complex industrial and manufacturing processes.
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Advances in iterative learning control with application to structural dynamic response reconstruction
… response reconstruction, fatigue testing, NARX models, Kolmogorov- Gabor polynomials, system identification, stable inversion, nonlinear, discrete time, Picard iteration, Mann iteration, quarter vehicle road simulator.
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Data-Driven Modeling and Real-Time Optimal Control of Continuous Manufacturing Processes
… identification and nonlinear deep learning (NARX) approaches, to optimize PID controller parameters through simulation-based gradient descent methods. A comprehensive experimental platform was developed to collect synchronized sensor and video data from a roll-to-roll continuous manufacturing …
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Modeling of Groundwater Heavy Metals and Methane Pollution for a Municipal Landfill Utilizing Data Driven and Numerical Modeling Techniques
… In the fourth part of the thesis, the two-stage NARX neural network model with meteorological input parameters was developed to predict daily CH4 generation rate from the landfill. The effects of each pre-processing step including minimizing collinearity problems, data normalization and filtering …
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Short-term sea level forecasting using machine learning techniques: A case study for South Africa
… to predict seawater levels. The open-loop NARX model was developed using the Neural Net Time Series application from the Deep Learning Toolbox 14.0 provided by MATLAB® (Mathworks, 2020). A total of five inputs (atmospheric pressure, mean wave period and direction, wind speed and direction) …
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Wavelet adaptive and predictive control with applications to chemical looping system
… nonlinear autoregressive exogenous (NARX) models without state or input constraints. The control inputs and wavelet model parameters are calculated by optimizing the cost function using gradient descent method. The convergence and stability of the proposed GPC scheme are proved using …
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Digital Twin for Machine Tools and Manufacturing Systems
… modelling case for rotating shafts using FEM and NARX networks. The second one is an industrial text-mining case involving over 2000 lines of historical fault records from an automotive connecting-rod production line. And the third one is an edge-intelligence prototype for gearbox monitoring built …
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Energy Management of Electric Vehicle Supply Equipment in Multi-Unit Residential Buildings
… Nonlinear Auto-Regressive with Exogenous Input (NARX) forecasting model is employed to predict non-EV load consumption, enabling real-time EMS operation. To manage the uncertainties introduced by non-EV load demand and stochastic EV availability across multiple sub-feeders, a Distributionally …
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Machine Learning for Modeling and Control of a Packaging Manufacturing Process
… process data as well as Neural network based NARX models and validate this combined plant model. We then use this model to test out various control strategies in simulation, ranging from classical PID and optimal linear control as well as use these models to further fine-tune these controllers …
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On the induction of temporal structure by recurrent neural networks
… Autoregressive Network with exogenous inputs (NARX) network and the multi-recurrent network (MRN). In addition to this, Echo State Networks (ESNs) are a relatively new class of recurrent neural network that do not suffer from the vanishing gradients problem and have been shown to exhibit …
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