Results for 'crossvalidation'

5 found
Order:
  1.  5
    Adaptive Hybrid Soft-Sensor Model of Grinding Process Based on Regularized Extreme Learning Machine and Least Squares Support Vector Machine Optimized by Golden Sine Harris Hawk Optimization Algorithm.Wei Xie, Jie-Sheng Wang, Cheng Xing, Sha-Sha Guo, Meng-wei Guo & Ling-Feng Zhu - 2020 - Complexity 2020:1-26.
    Soft-sensor technology plays a vital role in tracking and monitoring the key production indicators of the grinding and classifying process. Least squares support vector machine, as a soft-sensor model with strong generalization ability, can be used to predict key production indicators in complex grinding processes. The traditional crossvalidation method cannot obtain the ideal structure parameters of LSSVM. In order to improve the prediction accuracy of LSSVM, a golden sine Harris Hawk optimization algorithm was proposed to optimize the structure parameters (...)
    No categories
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark   1 citation  
  2.  12
    Machine-Learning Algorithm for Estimating Oil-Recovery Factor Using a Combination of Engineering and Stratigraphic Dependent Parameters.Kachalla Aliyuda & John Howell - 2019 - Interpretation 7 (3):SE151-SE159.
    The methods used to estimate recovery factor change through the life cycle of a field. During appraisal, prior to development when there are no production data, we typically rely on analog fields and empirical methods. Given the absence of a perfect analog, these methods are typically associated with a wide range of uncertainty. During plateau, recovery factors are typically associated with simulation and dynamic modeling, whereas in later field life, once the field drops off the plateau, a decline curve analysis (...)
    No categories
    Direct download (3 more)  
     
    Export citation  
     
    Bookmark  
  3.  3
    An Air Traffic Controller Action Extraction-Prediction Model Using Machine Learning Approach.Duc-Thinh Pham, Sameer Alam & Vu Duong - 2020 - Complexity 2020:1-19.
    In air traffic control, the airspace is divided into several smaller sectors for better management of air traffic and air traffic controller workload. Such sectors are usually managed by a team of two air traffic controllers: planning controller and executive controller. D-side controller is responsible for processing flight-plan information to plan and organize the flow of traffic entering the sector. R-side controller deals with ensuring safety of flights in their sector. A better understanding and predictability of D-side controller actions, for (...)
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark  
  4.  6
    Improving Hydrocarbon Exploration with Pore Pressure Assisted Earth-Model Building.Yangjun Liu, Michael O’Briain, Cara Hunter, Laura Jones & Emmanuel Saragoussi - 2018 - Interpretation: SEG 6 (3):SG41-SG47.
    In shale-dominated clastic lithology environments, a rock-physics model relating velocity and pore pressure can be calibrated and used to convert velocity to PP properties. The crossvalidation between velocity and overpressure, which follows the geology, can be used to better understand the model, help to build an initial velocity model, and allow selecting tomography solutions with more confidence. The velocity model developed using this approach is more plausible and more suitable for subsequent PP analysis. We highlight the application of this (...)
    No categories
    Direct download (2 more)  
     
    Export citation  
     
    Bookmark  
  5.  6
    Improving Hydrocarbon Exploration with Pore Pressure Assisted Earth-Model Building.Yangjun Liu, Michael O’Briain, Cara Hunter, Laura Jones & Emmanuel Saragoussi - 2018 - Interpretation 6 (3):SG41-SG47.
    In shale-dominated clastic lithology environments, a rock-physics model relating velocity and pore pressure can be calibrated and used to convert velocity to PP properties. The crossvalidation between velocity and overpressure, which follows the geology, can be used to better understand the model, help to build an initial velocity model, and allow selecting tomography solutions with more confidence. The velocity model developed using this approach is more plausible and more suitable for subsequent PP analysis. We highlight the application of this (...)
    No categories
    Direct download (3 more)  
     
    Export citation  
     
    Bookmark