Supervised Latent Factor Analysis for Process Data Regression Modeling and Soft Sensor Application


This transient proposed a new supervised latent factor analysis (FA) technique for method knowledge regression modeling. Totally different from the ancient principal part analysis/regression model, the new model will successfully estimate heterogeneous variances from totally different method variables, which is a lot of sensible. Underneath the identical probabilistic modeling framework, the single supervised latent FA model is further extended to the mixture form. Efficient expectation–maximization algorithms are developed for parameter learning in both single and mixture supervised latent FA models. Based on the regression modeling between simple-to-measure and difficult-to-live method variables, two soft sensors are engineered for quality prediction in the process. 2 case studies are provided to evaluate the modeling and performances of the new strategies.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : A Supervised Machine Learning Algorithm for Heart Rate Detection Using Doppler Motion-Sensing Radar ABSTRACT: The development of vital sign radar technology has shown to be an effective tool for measuring various
PROJECT TITLE : A General Approach for Achieving Supervised Subspace Learning in Sparse Representation ABSTRACT: A vast family of subspace learning algorithms based on dictionary learning has been developed during the last few
PROJECT TITLE : Online ADMM-based Extreme Learning Machine for Sparse Supervised Learning ABSTRACT: In the field of machine learning, sparse learning is a useful strategy for selecting features and avoiding overfitting. An online
PROJECT TITLE : Deeply-Supervised Networks With Threshold Loss for Cancer Detection in Automated Breast Ultrasound ABSTRACT: Automated breast ultrasound, often known as ABUS, is a novel and promising screening technique for the
PROJECT TITLE :Supervised Polarimetric SAR Image Classification Using Tensor Local Discriminant Embedding - 2018ABSTRACT:Feature extraction may be a terribly vital step for polarimetric artificial aperture radar (PolSAR) image

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry