دانلود مقاله ISI انگلیسی شماره 86883
ترجمه فارسی عنوان مقاله

مدل رگرسیون مدل آنلاین برای سیستم های تولید غیر خطی متفاوت زمان

عنوان انگلیسی
Online model regression for nonlinear time-varying manufacturing systems
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
86883 2017 11 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Automatica, Volume 78, April 2017, Pages 163-173

ترجمه کلمات کلیدی
رگرسیون مدل، رگرسیون فرآیند گاوسی، سیستم های تولید،
کلمات کلیدی انگلیسی
Model regression; Gaussian process regression; Manufacturing systems;
پیش نمایش مقاله
پیش نمایش مقاله  مدل رگرسیون مدل آنلاین برای سیستم های تولید غیر خطی متفاوت زمان

چکیده انگلیسی

This paper addresses the online modeling for time-varying manufacturing systems with random unknown model variations between production batches. By modeling the system as a Gaussian process, we first apply the standard Gaussian process regression (GPR) method for estimating the system model, which provides the optimal model estimate with the minimum mean square error (MSE). Then, an iterative form of the method is derived which is more computation efficient but maintains the estimation optimality. However, such optimality is obtained by continuously updating the covariances between the estimated model values and the measurements, which would make the storage and computation unaffordable when the control input can vary within an infinite control space. Due to such a limitation, a suboptimal interactive GPR method is further proposed by trading off the computation efficiency and the estimation accuracy, where the trade-off can be tuned by a designed parameter. Finally, effectiveness and performance of the proposed methods are demonstrated via both simulation and case study by comparing to the conventional nonlinear modeling methods.