Showing 841 - 860 results of 3,911 for search '"neural networks"', query time: 0.11s Refine Results
  1. 841

    Position/Force Tracking Impedance Control for Robotic Systems with Uncertainties Based on Adaptive Jacobian and Neural Network by Jinzhu Peng, Zeqi Yang, Tianlei Ma

    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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  2. 842
  3. 843

    Modeling and Error Compensation of Robotic Articulated Arm Coordinate Measuring Machines Using BP Neural Network by Guanbin Gao, Hongwei Zhang, Hongjun San, Xing Wu, Wen Wang

    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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  4. 844

    Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks by Narayanan Manikandan, Srinivasan Subha

    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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  9. 849

    A Full Stage Data Augmentation Method in Deep Convolutional Neural Network for Natural Image Classification by Qinghe Zheng, Mingqiang Yang, Xinyu Tian, Nan Jiang, Deqiang Wang

    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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  10. 850

    Study of the Workability of Self-Compacting Concrete (SCC) Using Experimental Methods and Artificial Neural Networks (ANN) by Amar Mezidi, Mourad Serikma, Salem Merabti

    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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  11. 851

    Learning Air Traffic as Images: A Deep Convolutional Neural Network for Airspace Operation Complexity Evaluation by Hua Xie, Minghua Zhang, Jiaming Ge, Xinfang Dong, Haiyan Chen

    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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  15. 855

    Analysis and Simulation of the Early Warning Model for Human Resource Management Risk Based on the BP Neural Network by Xue Yan, Xiangwu Deng, Shouheng Sun

    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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  16. 856

    Supply Chain Risk Prevention and Control Based on Fuzzy Influence Diagram and Discrete Hopfield Neural Network by Xin Su, Maohua Zhong

    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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  17. 857

    Research on Adaptive Trajectory Tracking Algorithm for a Quadrotor Based on Backstepping and the Sigma-Pi Neural Network by Zhiming Chen, Kang Niu, Lei Li

    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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  18. 858

    Stability in Switched Cohen-Grossberg Neural Networks with Mixed Time Delays and Non-Lipschitz Activation Functions by Huaiqin Wu, Guohua Xu, Chongyang Wu, Ning Li, Kewang Wang, Qiangqiang Guo

    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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  19. 859
  20. 860

    Assessment of a Neural-Network-Based Optimization Tool: A Low Specific-Speed Impeller Application by Matteo Checcucci, Federica Sazzini, Michele Marconcini, Andrea Arnone, Mario Coneri, Luigi De Franco, Matteo Toselli

    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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