کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7104355 | 1460339 | 2018 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Semisupervised learning for probabilistic partial least squares regression model and soft sensor application
ترجمه فارسی عنوان
یادگیری نیمه حفاظتی برای مدل رگرسیون حداقل مربعات احتمالی و نرم افزار حسگر نرم
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
حداقل مربعات جزئی جزئی احتمالی، مدل سازی رگرسیون، انتظار برای به حداکثر رساندن، مدل سازی اطلاعات نیمه نگهداری،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی شیمی
تکنولوژی و شیمی فرآیندی
چکیده انگلیسی
Due to long sampling time and large measurement delay, variables such as melt index, concentrations of key components in the stream, and product quality variables are difficult to measure online. At the same time, routinely recorded variables such as flow, temperature and press are much easier to measure. As a result, only a small portion of data has values for all variables, while other large parts of data only have values for those routinely recorded variables. Focused on regression modeling between those two types of process variables with imbalanced sampling values, this paper develops a semisupervised form of the Probabilistic Partial Least Squares (PPLS) model. In this model, both labeled data samples (with values for both two types of variables) and unlabeled data samples (with values only for routinely recorded variables) can be effectively used. For parameter learning of the semisupervised PPLS model, an efficient Expectation-Maximization algorithm is designed. An industrial case study is provided as an example for soft sensor application, which is constructed based on the new developed model.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Process Control - Volume 64, April 2018, Pages 123-131
Journal: Journal of Process Control - Volume 64, April 2018, Pages 123-131
نویسندگان
Junhua Zheng, Zhihuan Song,