Search Results - "the backpropagation algorithm"

Learning Online MEMS Calibration with Time-Varying and Memory-Efficient Gaussian Neural Topologies by Pau, Danilo Pietro, Tognocchi, Simone, Marcon, Marco

“… The real-time sensor data are subject to errors, such as time-varying bias and thermal stress…”
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Artificial neural network: A new diagnostic posturographic tool for disorders of stance by Krafczyk, Siegbert, Tietze, Simon, Swoboda, Walter, Valkovič, Peter, Brandt, Thomas

“…), and acute unilateral vestibular neuritis (VN). A standard 3-layer feed-forward ANNW, using the backpropagation algorithm, was trained with TCs, validated with VCs, and its accuracy tested on 5 new cases…”
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NeuroTrajectory: A Neuroevolutionary Approach to Local State Trajectory Learning for Autonomous Vehicles by Grigorescu, Sorin Mihai, Trasnea, Bogdan, Marina, Liviu, Vasilcoi, Andrei, Cocias, Tiberiu

“…). Although the cost function used for training can aggregate multiple weighted objectives, the gradient descent step is computed by the backpropagation algorithm using a single-objective loss…”
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An Evaluation Model of Urban-Rural Exchange Teachers in Elementary School Based on Optimal Control Neural Network by Chen, Ke

“…In this paper, an optimal control neural network algorithm is used to conduct an in-depth study and analysis of the evaluation of elementary school urban-rural…”
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Robust Finite-Time Adaptive Nonlinear Control System for an EOD Robotic Manipulator: Design, Implementation, and Experimental Validation by Ccari, Luis F. Canaza, Aguilar, Walker, Supo, Elvis, Espinoza, Erasmo Sulla, Vidal, Yuri Silva, Medina, Nicolas, Pari, Lizardo

“…) robotic manipulator, considering the actuator dynamics and subject to external disturbances and uncertainties…”
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A NEW APPROACH TO BUILDING ENERGY MODELS OF NEURAL NETWORKS by Parzhyn, Yurii, Lapin, Mykyta, Bokhan, Kostiantyn

“…Relevance. Modern artificial neural network models require significant energy and other resources for training and operation. Training generative models…”
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Data-Driven Approach to Predict Spot Market Price in Indian Electricity by M. P., Subeekrishna, R. R., Lekshmi

“… The electricity market is subject to several constraints imposed by the inherent nature of energy, which necessitates perpetual parity between consumption and production on a continental scale…”
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Circulating MicroRNAs and Both Association with Methacholine PC20 and Prediction of Asthma Exacerbation in the Childhood Asthma Management Program (CAMP) Cohort by Davis, Joshua Seth

“…Background: Circulating microRNAs have shown promise both as a non-invasive biomarker and a predictor of disease activity. Prior asthma studies with clinical,…”
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Conditional market segmentation by neural networks: a Monte-Carlo study by Natter, Martin

“… (psychographics/sociodemographics). Parameters of all models are estimated by the backpropagation algorithm…”
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Modélisation pluie-débit en région tropicale humide : application des réseaux de neurones sur quatre stations hydrométriques du Bandama Blanc (Bada, Marabadiassa, Tortiya et Bou) situées au Nord de la Côte d'Ivoire. Thèse de l'Université de Cocody (Côte d'Ivoire), 2007, 219 p by Yao Blaise Koffi

“…The rainfall-runoff relationship is the subject of many studies because of its importance in the implementation of many development projects…”
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Generalizing Backpropagation for Gradient-Based Interpretability by Du, Kevin, Lucas Torroba Hennigen, Stoehr, Niklas, Warstadt, Alexander, Cotterell, Ryan

“… This observation allows us to generalize the backpropagation algorithm to efficiently compute other interpretable statistics about the gradient graph of a neural network, such as the highest-weighted path and entropy…”
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NeuroTrajectory: A Neuroevolutionary Approach to Local State Trajectory Learning for Autonomous Vehicles by Grigorescu, Sorin, Trasnea, Bogdan, Liviu Marina, Vasilcoi, Andrei, Cocias, Tiberiu

“…). Although the cost function used for training can aggregate multiple weighted objectives, the gradient descent step is computed by the backpropagation algorithm using a single-objective loss…”
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Development and testing of a neuro-fuzzy classification system for IOS data in asthmatic children by Maduko, Elizabeth

“… after it is adequately trained with a data set containing data patterns sampled from both healthy and unhealthy subjects…”
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Comparison of Traditional and Evolutionary Neural Networks for Classification by Alkaya, Asil

“…Classification refers to the assignment of a finite set of alternatives into predefined groups. The limitation of the statistical models applied to the…”
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Transputer Implementation of the EKF-Based Learning Algorithm for Multilayered Neural Networks used in Classification of EEG Signals by Rao, K Deergha, Reddy, D C

“… However, the speed of convergence of the backpropagation algorithm is rather slow. The Extended Kalman Filtering (EKF…”
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Conditional market segmentation by neural networks by Natter, M.

“… (psychographics/sociodemographics). Parameters of all models are estimated by the backpropagation algorithm…”
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Neural network classification of EEG signals using time-frequency representation by Gope, C., Kehtarnavaz, N., Nair, D.

“… The texture features are fed into a three-layer neural network classifier trained by the backpropagation algorithm…”
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Unsupervised global optimization: applications on classification of handwritten digits and visual evoked potentials by Micheli-Tzanakou, E., Dasey, T.J.

“…) classification using two neural network learning paradigms. The first is an unsupervised approach, trained by the combinatorial optimization routine ALOPEX, while the second method uses the backpropagation algorithm…”
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Neural networks for perceptual grouping by Sarkaria, Sarbjit Singh

“… However, despite the benefits of such an approach it is not at all obvious how networks can be developed which are capable of recognising objects subject to changes in rotation, translation and viewpoint…”
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A decision support system for the classification of event-related potentials by Vasios, C.E., Matsopoulos, G.K., Nikita, K.S., Uzunoglu, N., Papageorgiou, C.

“… The classification level is implemented with a single three-layer neural network, trained with the backpropagation algorithm and classifies the data into two classes…”
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