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A Fast and Robust Gas Recognition Algorithm Based on Hybrid Convolutional and Recurrent Neural Network
Published 2019“…Featuring the capability of learning the correlations of time-series data, the proposed deep learning method is well-suited for extracting the valuable transient feature contained in the very beginning of the response curve. …”
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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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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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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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Deep Learning-Based Coding Strategy for Improved Cochlear Implant Speech Perception in Noisy Environments
Published 2025“…These processes begin with capturing speech in analog form and applying signal processing algorithms to ensure compatibility with devices like cochlear implants (CIs). …”
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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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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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Nonlinear analysis of shell structures using image processing and machine learning
Published 2023“…The proposed approach can be significantly more efficient than training a machine learning algorithm using the raw numerical data. To evaluate the proposed method, two different structures are assessed where the training data is created using nonlinear finite element analysis. …”
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Personalization in Real-Time Physical Activity Coaching Using Mobile Applications: A Scoping Review
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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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CNN and HEVC Video Coding Features for Static Video Summarization
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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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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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Reinforcement R-learning model for time scheduling of on-demand fog placement
Published 2020“…This implies choosing the right time and best volunteer to create a fog which the cloud can benefit from is essential. …”
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