Showing 3,301 - 3,320 results of 26,283 for search 'Nurgal~', query time: 5.83s Refine Results
  1. 3301

    Finite-Time Synchronization of Fractional-Order Complex-Valued Cohen-Grossberg Neural Networks with Mixed Time Delays and State-Dependent Switching by Xiaoxia Li, Yingzi Cao, Chi Zheng, Zhixin Feng, Guizhi Xu

    Published 2022-01-01
    “…This paper discussed the finite-time synchronization of fractional-order complex-valued Cohen-Grossberg neural networks (FCVCGNNs), which contain mixed time delays and state-dependent switching that make the model more comprehensive. …”
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    GIS-Based Landslide Susceptibility Mapping Using Information, Frequency Ratio, and Artificial Neural Network Methods in Qinghai Province, Northwestern China by Bin Li, Nianqin Wang, Jing Chen

    Published 2021-01-01
    “…In this paper, the information method (IM) model, frequency ratio (FR) model, and artificial neural network (ANN) model are used to evaluate the susceptibility of geological hazards, and the receiver operating characteristic (ROC) curve of disaster points at different levels is used to test the evaluation accuracy of three models. …”
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  5. 3305
  6. 3306

    Delay-Range-Dependent Global Robust Passivity Analysis of Discrete-Time Uncertain Recurrent Neural Networks with Interval Time-Varying Delay by Chien-Yu Lu, Chin-Wen Liao, Hsun-Heng Tsai

    Published 2009-01-01
    “…This paper examines a passivity analysis for a class of discrete-time recurrent neural networks (DRNNs) with norm-bounded time-varying parameter uncertainties and interval time-varying delay. …”
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  7. 3307

    A Fault Diagnosis Method for Oil Well Pump Using Radial Basis Function Neural Network Combined with Modified Genetic Algorithm by Deliang Yu, Yanmei Li, Hao Sun, Yulong Ren, Yongming Zhang, Weigui Qi

    Published 2017-01-01
    “…This paper presents a new method to diagnose oil well pump faults using a modified radial basis function neural network. With the development of submersible linear motor technology, rodless pumping units have been widely used in oil exploration. …”
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  13. 3313

    Drivers of high rates of carbon burial in a riverine-influenced freshwater marsh in the Long Point Walsingham Priority Place of southern Ontario by Amanda L. Loder, Adam Gillespie, Omid Haeri Ardakani, Cecilia Cordero Oviedo, Sarah A. Finkelstein

    Published 2025-01-01
    “…We demonstrate that elevated recent rates of SOC accumulation are largely explained by more labile carbon in surficial soils, and are sustained for less than a century before transitioning to slower burial rates of predominantly recalcitrant organic matter. …”
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  17. 3317

    Adaptation and Molecular Characterization of Two Malaysian Very Virulent Infectious Bursal Disease Virus Isolates Adapted in BGM-70 Cell Line by Nafi’u Lawal, Mohd Hair-Bejo, Siti Suri Arshad, Abdul Rahman Omar, Aini Ideris

    Published 2017-01-01
    “…Two Malaysian very virulent infectious bursal disease virus (vvIBDV) strains UPM0081 and UPM190 (also known as UPMB00/81 and UPM04/190, respectively) isolated from local IBD outbreaks were serially passaged 12 times (EP12) in specific pathogen free (SPF) chicken embryonated eggs (CEE) by chorioallantoic membrane (CAM) route. …”
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  18. 3318
  19. 3319

    Predictive Modeling of Total Real and Reactive Power Losses in Contingency Systems Using Function-Fitting Neural Networks with Graphical User Interface by Alfredo Bonini Neto, Alexandre de Queiroz, Giovana Gonçalves da Silva, André Gifalli, André Nunes de Souza, Enio Garbelini

    Published 2025-01-01
    “…The key advantage of this methodology is its speed, allowing quick estimation of power loss curves both in normal and contingency conditions, whether mild or severe. …”
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  20. 3320

    A New Artificial Neural Network Model for the Prediction of the Effect of Molar Ratios on Compressive Strength of Fly Ash-Slag Geopolymer Mortar by Shaise K. John, Alessio Cascardi, Yashida Nadir, Maria Antonietta Aiello, K. Girija

    Published 2021-01-01
    “…In this study, an Artificial Neural Network (ANN) model has been applied to predict the effect of molar ratios on the 28-day compressive strength of fly ash-slag geopolymer mortar. …”
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