Development of Adaptive Soft Sensor Using Locally Weighted Kernel Partial Least Square Model
نویسندگان
چکیده
منابع مشابه
Kernel least mean square with adaptive kernel size
Kernel adaptive filters (KAF) are a class of powerful nonlinear filters developed in Reproducing Kernel Hilbert Space (RKHS). The Gaussian kernel is usually the default kernel in KAF algorithms, but selecting the proper kernel size (bandwidth) is still an open important issue especially for learning with small sample sizes. In previous research, the kernel size was set manually or estimated in ...
متن کاملCo-learning with a locally weighted partial least squares for soft sensors of nonlinear processes
A method to improve adaptivity of soft sensors is investigated in this paper. Soft sensors have become very important in the chemical industry to achieve a highly efficient, high-quality and safe production system. Among the various methods, partial least squares (PLS) method is the most used for soft sensors. In this research, a co-learning style locally weighted PLS method which utilizes a se...
متن کاملKernel Least Mean Square Algorithm
A simple, yet powerful, learning method is presented by combining the famed kernel trick and the least-mean-square (LMS) algorithm, called the KLMS. General properties of the KLMS algorithm are demonstrated regarding its well-posedness in very high dimensional spaces using Tikhonov regularization theory. An experiment is studied to support our conclusion that the KLMS algorithm can be readily u...
متن کاملWeighted least square ensemble networks
Ensemble of networks has been proven to give better prediction result than a single network. Two commonly used way of determining the ensemble weights are simple average ensemble method and the generalized ensemble method. In the paper, we propose the weighted least square ensemble network. The major difference between this method and the other ensemble methods is that we do not assume that nei...
متن کاملEstimation of active pharmaceutical ingredients content using locally weighted partial least squares and statistical wavelength selection.
Development of quality estimation models using near infrared spectroscopy (NIRS) and multivariate analysis has been accelerated as a process analytical technology (PAT) tool in the pharmaceutical industry. Although linear regression methods such as partial least squares (PLS) are widely used, they cannot always achieve high estimation accuracy because physical and chemical properties of a measu...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Chemical Product and Process Modeling
سال: 2017
ISSN: 1934-2659
DOI: 10.1515/cppm-2017-0022