Search Results - (( data optimization methods algorithm ) OR ( data estimation means algorithm ))
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System identification using Extended Kalman Filter
Published 2017“…Besides, Extended Kalman Filter (EKF) algorithm was selected in this project as an algorithm for offline estimation data purposes. …”
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Student Project -
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Statistical data preprocessing methods in distance functions to enhance k-means clustering algorithm
Published 2018“…The K-Means algorithm is the commonest and fast technique in partitional cluster algorithms, although with unnormalized datasets it can achieve local optimal. …”
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Thesis -
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Parameter Estimation of Lorenz Attractor: A Combined Deep Neural Network and K-Means Clustering Approach
Published 2022“…After that, it has been suggested to improve the efficiencies in the Deep Neural Network (DNN) model by combining the DNN with an unsupervised machine learning algorithm, the K-Means clustering algorithm. This study constructs the flow of DNN based method with the K-Means algorithm. …”
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A new hybrid deep neural networks (DNN) algorithm for Lorenz chaotic system parameter estimation in image encryption
Published 2023“…Hybridizing the Deep Neural Network (DNN) with the K-Means Clustering algorithm will increase the accuracy and reduce the data complexity of the Lorenz dataset. …”
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Analysis of toothbrush rig parameter estimation using different model orders in Real-Coded Genetic Algorithm (RCGA)
Published 2018“…Realcoded genetic algorithm (RCGA) as a stochastic global search method was applied for optimization. …”
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Kinetic parameters estimation of the Escherichia coli (E. coli) model by Garra Rufa-inspired Optimization Algorithm (GRO)
Published 2024“…In this study, experimental data was used to estimate seven kinetic parameters. …”
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Robust Portfolio Mean-Variance Optimization for Capital Allocation in Stock Investment Using the Genetic Algorithm: A Systematic Literature Review
Published 2024journal::journal article -
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A novel peak detection algorithm using particle swarm optimization for chew count estimation of a contactless chewing detection
Published 2022“…Overall, the proposed methods achieve a 4.26% mean absolute error of chewing count estimation.…”
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Analysis of Toothbrush Rig Parameter Estimation Using Different Model Orders in Real Coded Genetic Algorithm (RCGA)
Published 2024“…Real-coded genetic algorithm (RCGA) as a stochastic global search method was applied for optimization. …”
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State of charge estimation in lithium-ion batteries: A neural network optimization approach
Published 2023“…This paper proposes an enhanced data-driven method based on a time-delay neural network (TDNN) algorithm for state of charge (SOC) estimation in lithium-ion batteries. …”
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A novel peak detection algorithm using particle swarm optimization for chew count estimation of a contactless chewing detection
Published 2022“…Overall, the proposed methods achieve a 4.26% mean absolute error of chewing count estimation.…”
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Integration of machine learning and remote sensing for above ground biomass estimation through Landsat-9 and field data in temperate forests of the Himalayan region
Published 2024“…Through the utilization of openly accessible fine-resolution data and employing the RF algorithm, the research demonstrated promising outcomes in the identification of optimal predictor-algorithm combinations for forest AGB mapping. …”
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Automatic database of robust neural network forecasting / Saadi Ahmad Kamaruddin, Nor Azura Md. Ghani and Norazan Mohamed Ramli
Published 2014“…The direct idea of making the conventional neural network learning algorithm more powerful towards outlying data is by replacing the mean square error (MSE) with a different symmetric and continuous cost function. …”
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Book Section -
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Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm
Published 2025Subjects:Article -
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Parameter estimation of stochastic differential equation
Published 2012“…Non-parametric modeling is a method which relies heavily on data and motivated by the smoothness properties in estimating a function which involves spline and non-spline approaches. …”
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Physics-guided deep neural network to characterize non-Newtonian fluid flow for optimal use of energy resources
Published 2021“…The statistical error estimation exhibits a mean absolute error of 11.5, and root mean squared error of 0.87. …”
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