Search Results - (( model validation a algorithm ) OR ( rate estimation system algorithm ))
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Fractional chaotic ensemble particle swarm optimizer for identifying the single, double, and three diode photovoltaic models� parameters
Published 2023“…Heuristic algorithms; Particle swarm optimization (PSO); Photovoltaic cells; Renewable energy resources; Solar power generation; Commercial applications; Environmental conditions; Fast convergence rate; Meta heuristic algorithm; Optimization algorithms; Parameters estimation; Particle swarm optimizers; PV models; Diodes; algorithm; alternative energy; electrode; model; optimization; parameter estimation; photovoltaic system; temperature effect…”
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Differential Search Optimized Random Forest Regression Algorithm for State of Charge Estimation in Electric Vehicle Batteries
Published 2023Conference Paper -
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End-to-end DVB-S2X system design with deep learning-based channel estimation over satellite fading channels
Published 2021“…In the fourth part a deep learning (DL) algorithm of channel estimation for two fad�ing channel models, Tropical and Temperate in the satellite communication system is presented. …”
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Parameter estimation of multicomponent transient signals using deconvolution and ARMA modelling techniques
Published 2003“…This approach also provides a graphical procedure for detecting and validating the number of exponential signals present in the data. …”
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Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm
Published 2025“…However, existing RUL prediction approaches have difficulties with variability and nonlinearity that occur during battery degradation, data extraction, feature extraction, hyperparameters optimization, and prediction model uncertainty. To address these problems, this paper introduces a novel hybrid approach for RUL prediction, combining a Lightning Search Algorithm (LSA) with a Long-Short Term Memory (LSTM) deep learning model. …”
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Modeling and multi-objective optimal sizing of standalone photovoltaic system based on evolutionary algorithms
Published 2020“…Firstly, an improved EM (IEM) algorithm is presented to estimate the five parameters of the single PV-module system. …”
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Automatic control of flotation process using computer vision
Published 2015“…A froth model correlating the image variables to process variables and a prediction system estimating the metallurgical parameters based on image variables were then developed by using a neural network structure. …”
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Hybrid fuzzy-sliding mode observer design for estimation and advanced control of an ethylene polymerization process / Jarinah Mohd Ali
Published 2017“…Due to high-nonlinearities of the chemical process systems, a single observer may not be sufficient to estimate the variables resulting in offsets and slow estimation rates. …”
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Decentralized Adaptive Pi With Adaptive Interaction Algorithm Of Wastewater Treatment Plant
Published 2014“…The error function is minimized directly by approximate Frechet tuning algorithm without explicit estimation of the model. …”
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Control of IC Engine: Design a Novel MIMO Fuzzy Backstepping Adaptive Based Fuzzy Estimator Variable Structure Control.
Published 2011“…This paper expands a Multi Input Multi Output (MIMO) fuzzy estimator variable structure control (VSC) which controller coefficient is on-line tuned by fuzzy backstepping algorithm. …”
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Satellite based quantitative rainfall estimation for flash flood forecasting / Wardah Tahir
Published 2008“…In this study, a rainfall estimation algorithm using the information from the geostationary meteorological satellite infrared (IR) images is developed for potential input to a flood forecasting system. …”
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Development of a Universal Artificial Neural Network Model for Pressure Loss Estimation in Pipeline Systems; A comparative Study
Published 2010“…A total number of data points consists of 335 sets has been used for generating, validating, and testing the ANN model. …”
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Prediction of epidemic peak and infected cases for COVID-19 disease in Malaysia, 2020
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DEVELOPMENT AND TESTING OF UNIVERSAL PRESSURE DROP MODELS IN PIPELINES USING ABDUCTIVE AND ARTIFICIAL NEURAL NETWORKS
Published 2011“…For the ANN model, a ratio of 2: 1: 1 between training, validation, and testing sets yielded the best training/testing performance. …”
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