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841
Position/Force Tracking Impedance Control for Robotic Systems with Uncertainties Based on Adaptive Jacobian and Neural Network
Published 2019-01-01“…In this paper, an adaptive Jacobian and neural network based position/force tracking impedance control scheme is proposed for controlling robotic systems with uncertainties and external disturbances. …”
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842
VECTOR: Velocity-Enhanced GRU Neural Network for Real-Time 3D UAV Trajectory Prediction
Published 2024-12-01Subjects: Get full text
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843
Modeling and Error Compensation of Robotic Articulated Arm Coordinate Measuring Machines Using BP Neural Network
Published 2017-01-01“…In this paper, a modeling and error compensation method for AACMM is proposed based on BP Neural Networks. According to the available measurements, the poses of the AACMM are used as the input, and the coordinates of the probe are used as the output of neural network. …”
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844
Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks
Published 2016-01-01“…For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. …”
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845
Thermal design of a thermoelectric refrigerator operating near room temperature using artificial neural network
Published 2025-01-01Subjects: “…Artificial neural networks…”
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846
A neural network design for black-box identification of converter impedance models in arbitrary operating conditions
Published 2025-01-01Subjects: Get full text
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847
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848
Barriers and enhance strategies for green supply chain management using continuous linear diophantine neural networks
Published 2025-01-01Subjects: “…Neural network…”
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849
A Full Stage Data Augmentation Method in Deep Convolutional Neural Network for Natural Image Classification
Published 2020-01-01“…In this paper, we propose a full stage data augmentation framework to improve the accuracy of deep convolutional neural networks, which can also play the role of implicit model ensemble without introducing additional model training costs. …”
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850
Study of the Workability of Self-Compacting Concrete (SCC) Using Experimental Methods and Artificial Neural Networks (ANN)
Published 2024-05-01“…Three methods are considered: the first is an empirical method represented by an approach based on mortar optimization, a solution proposed by Japanese researchers who originally introduced the concept of self-compacting concrete; the second is a graphical method by Dreux-Gorisse used for ordinary concrete, which optimizes the composition of the aggregate skeleton by selecting fractions without additives and superplasticizers; and the third is a statistical method that we developed using an approach based on Artificial Neural Networks (ANN) built from a database from previous research projects. …”
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851
Learning Air Traffic as Images: A Deep Convolutional Neural Network for Airspace Operation Complexity Evaluation
Published 2021-01-01“…To overcome these problems, this paper for the first time proposes an end-to-end SOC learning framework based on deep convolutional neural network (CNN) specifically for free of hand-crafted factors environment. …”
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852
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853
Estimating the visibility in foggy weather based on meteorological and video data: A Recurrent Neural Network approach
Published 2023-01-01Subjects: Get full text
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854
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855
Analysis and Simulation of the Early Warning Model for Human Resource Management Risk Based on the BP Neural Network
Published 2020-01-01“…Based on the summary and analysis of previous research works, this article expounded the research status and significance of early warning for human resource management risks, elaborated the development background, current status, and future challenges of the BP neural network, introduced the method and principle of the BP neural network’s connection weight calculation and learning training, performed the risk inducement analysis, index system establishment, and network node selection of human resource management, constructed an early warning model of human resource management risk based on the BP neural network, conducted the risk warning model training and detection based on the BP neural network, and finally carried out a simulation and its result analysis. …”
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856
Supply Chain Risk Prevention and Control Based on Fuzzy Influence Diagram and Discrete Hopfield Neural Network
Published 2021-01-01“…To explore different influence degrees of multirisk factors and multilinks on enterprises, we propose a supply chain risk prevention and control model based on a fuzzy influence diagram and Hopfield neural network. Using the model that both calculates the risk size and occurrence probability of the supply chain and allows identifying various risk prevention and control levels, the supply chain risk is evaluated both objectively and fairly. …”
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857
Research on Adaptive Trajectory Tracking Algorithm for a Quadrotor Based on Backstepping and the Sigma-Pi Neural Network
Published 2019-01-01“…Then a new trajectory tracking algorithm is designed by using the sigma-pi neural network and backstepping. The paper designs the sigma-pi neural network compensation control law and gives the Lyapunov-type stability analysis. …”
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858
Stability in Switched Cohen-Grossberg Neural Networks with Mixed Time Delays and Non-Lipschitz Activation Functions
Published 2012-01-01“…The stability for the switched Cohen-Grossberg neural networks with mixed time delays and α-inverse Hölder activation functions is investigated under the switching rule with the average dwell time property. …”
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859
Neural Network-Based Lower Limb Prostheses Control Using Super Twisting Sliding Mode Control
Published 2025-01-01Subjects: “…Artificial neural network (ANN)…”
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860
Assessment of a Neural-Network-Based Optimization Tool: A Low Specific-Speed Impeller Application
Published 2011-01-01“…The design procedure relies on a modern optimization technique such as an Artificial-Neural-Network-based approach (ANN). The impeller geometry is parameterized in order to allow geometrical variations over a large design space. …”
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