Semi-Supervised Hybrid Local Kernel Regression for Soft Sensor Modelling of Rubber-Mixing Process
نویسندگان
چکیده
منابع مشابه
Composite Kernel Optimization in Semi-Supervised Metric
Machine-learning solutions to classification, clustering and matching problems critically depend on the adopted metric, which in the past was selected heuristically. In the last decade, it has been demonstrated that an appropriate metric can be learnt from data, resulting in superior performance as compared with traditional metrics. This has recently stimulated a considerable interest in the to...
متن کاملAn adaptive neuro-fuzzy inference system as a soft sensor for viscosity in rubber mixing process
Mixing rubber in an internal mixer is a complex nonlinear process in which viscosity of the rubber is one of the key quantities concerning end product quality. Since viscosity can’t be measured online, soft sensor methods for modelling viscosity are investigated to establish an online control of viscosity. This paper presents a black-box approach to modelling viscosity using an adaptive neuro-f...
متن کاملSemi-supervised Penalized Output Kernel Regression for Link Prediction
Link prediction is addressed as an output kernel learning task through semi-supervised Output Kernel Regression. Working in the framework of RKHS theory with vectorvalued functions, we establish a new representer theorem devoted to semi-supervised least square regression. We then apply it to get a new model (POKR: Penalized Output Kernel Regression) and show its relevance using numerical experi...
متن کاملSemi-supervised Gaussian Process Ordinal Regression
Ordinal regression problem arises in situations where examples are rated in an ordinal scale. In practice, labeled ordinal data are difficult to obtain while unlabeled ordinal data are available in abundance. Designing a probabilistic semi-supervised classifier to perform ordinal regression is challenging. In this work, we propose a novel approach for semi-supervised ordinal regression using Ga...
متن کاملWavelet-Kernel Estimation of Regression Function for Uniformly Mixing Process
Methods of estimation of density and regression function are quite common in statistical applications. Wavelet theory has the potential to provide statisticians with powerful new techniques for nonparametric inference. It combines recent advances in approximation theory with insights gained from applied signal analysis. Nonparametric curve estimation by wavelets has been treated in numerous art...
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: Advances in Polymer Technology
سال: 2020
ISSN: 0730-6679,1098-2329
DOI: 10.1155/2020/6981302