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Machine Learning-Driven Prediction of Corrosion Inhibitor Efficiency: Emerging Algorithms, Challenges, and Future Outlooks
Published 2025“…<p dir="ltr">Machine learning (ML) frameworks are transforming the development of corrosion inhibitors by enabling quantitative prediction of inhibition efficiency before synthesis. …”
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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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Learning continuous functions using decision tree learning algorithms
Published 2001Get full text
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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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Prediction of EV Charging Behavior Using Machine Learning
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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“…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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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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Optimal selection of the forgetting matrix into an iterative learning control algorithm
Published 2005“…A recursive optimal algorithm, based on minimizing the input error covariance matrix, is derived to generate the optimal forgetting matrix and the learning gain matrix of a P-type iterative learning control (ILC) for linear discrete-time varying systems with arbitrary relative degree. …”
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Teaching–learning-based optimization algorithm: analysis study and its application
Published 2024Subjects: “…Teaching–learning-based optimization algorithm…”
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Stochastic P-type/D-type iterative learning control algorithms
Published 2003“…This paper presents stochastic algorithms that compute optimal and sub-optimal learning gains for a P-type iterative learning control algorithm (ILC) for a class of discrete-time-varying linear systems. …”
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Leveraging Machine and Deep Learning Algorithms for hERG Blocker Prediction
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Wild Blueberry Harvesting Losses Predicted with Selective Machine Learning Algorithms
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Personalization in Real-Time Physical Activity Coaching Using Mobile Applications: A Scoping Review
Published 2019Subjects: -
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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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