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Bird’s Eye View feature selection for high-dimensional data
Published 2023“…BEV incorporates elements of Evolutionary Algorithms with a Genetic Algorithm to maintain a population of top-performing agents, Dynamic Markov Chain to steer the movement of agents in the search space, and Reinforcement Learning to reward and penalize agents based on their progress. …”
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Prediction of pressure gradient for oil-water flow: A comprehensive analysis on the performance of machine learning algorithms
Published 2022“…<p dir="ltr">Pressure gradient (PG) in liquid-liquid flow is one of the key components to design an energy-efficient transportation system for wellbores. This study aims to develop five robust machine learning (ML) algorithms and their fusions for a wide range of flow patterns (FP) regimes. …”
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Machine Learning-Driven Prediction of Corrosion Inhibitor Efficiency: Emerging Algorithms, Challenges, and Future Outlooks
Published 2025“…Ensemble schemes such as Gaussian process regression with simple averaging and gradient boosting regressors fortified by permutation feature importance improve robustness in noisy or multi-alloy environments. At the same time, virtual sample augmentation and genetic algorithm feature selection elevate sparse data performance, raising k-nearest neighbor models from R<sup>2</sup> = 0.05 to 0.99 in a representative thiophene set. …”
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A discrete-time learning control algorithm
Published 1994“…A discretized version of the D-type learning control algorithm is presented for a MIMO linear discrete-time system. …”
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Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques
Published 2022“…Nowadays, learning-based modeling methods are utilized to build a precise forecast model for renewable power sources. …”
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A stochastic iterative learning control algorithm with application to an induction motor
Published 2004“…A recursive optimal algorithm, based on minimizing the input error covariance matrix, is derived to generate the learning gain matrix of a P-type ILC for linear discrete-time varying systems with arbitrary relative degree. …”
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Estimating Construction Project Duration Using a Machine Learning Algorithm
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Discrete-time learning control algorithm for a class of nonlinear systems
Published 1995“…Applies a discrete-time learning algorithm to a class of discrete-time varying nonlinear system. …”
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A discrete-time stochastic iterative learning control algorithm for a class of nonlinear systems
Published 2005“…This article presents a stochastic algorithm that computes the learning gain matrix of a “D-type iterative learning control (ILC) algorithm for a class of discrete-time varying nonlinear systems with linear input/output actions having relative degree one. …”
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A discrete-time learning control algorithm for a class of linear time-invariant systems
Published 1995“…A discretized version of the D-type learning control algorithm is presented for a MIMO linear discrete-time system. …”
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A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text
Published 2019“…The previously published research on Arabic ontology learning from text falls into three categories: developing manually hand-crafted rules, using ordinary supervised/unsupervised machine learning algorithms, or a hybrid of these two approaches. …”
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Prediction of EV Charging Behavior Using Machine Learning
Published 2021“…Using data-driven tools and machine learning algorithms to learn the EV charging behavior can improve scheduling algorithms. …”
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Learning control algorithms for tracking "slowly" varying trajectories
Published 1997“…This is due to the requirement that all learning algorithms assume that a desired output is given a priori over the time duration t /spl isin/ ~0,T\. …”
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Selection of the learning gain matrix of an iterative learning control algorithm in presence of measurement noise
Published 2005“…The state function does not need to satisfy a Lipschitz condition. This work also provides a recursive algorithm that generates the appropriate learning gain functions that meet the arbitrary high precision output tracking objective. …”
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Robustness and convergence rate of a discrete‐time learning control algorithm for a class of nonlinear systems
Published 1999“…In this paper, we apply a discrete‐time learning algorithm to a class of discrete‐time varying nonlinear systems with affine input action and linear output having relative degree one. …”
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Multiclass feature selection with metaheuristic optimization algorithms: a review
Published 2022“…For this reason, this paper presents a systematic survey of literature for solving multiclass feature selection problems utilizing metaheuristic algorithms that can assist classifiers selects optima or near optima features faster and more accurately. …”
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Exploring Semi-Supervised Learning Algorithms for Camera Trap Images
Published 2022“…A Master of Science thesis in Computer Engineering by Ali Reza Sajun entitled, “Exploring Semi-Supervised Learning Algorithms for Camera Trap Images”, submitted in August 2022. …”
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