Research Article
Online Semisupervised Learning Approach for Quality Monitoring of Complex Manufacturing Process
| | Input: partially labelled data batches: | | | for data batch do | | | Testing and update performance metrics | | | if k < S then {S: initialization batch number} | | | for epochs = 1:E do | | | Update ACM | | | | | | {gen:generative phase} | | | for alldo | | | Structural evolution | | | | | | Calculate { in (1)} | | | end for | | | | | | for alldo {dis:discriminative phase} | | | Structural evolution | | | | | | Calculate { in (1)} | | | end for | | | end for | | | else | | | Update ACM | | | if exists unlabelled data then | | | Generate pseudolabel via (2) | | | end if | | | | | | | | | Calculate { in (1)} | | | for alldo | | | Structural evolution | | | | | | end for | | | | | | for alldo | | | Structural evolution | | | | | | Calculate { in (1)} | | | Update net with R in (1) {autoregularization} | | | end for | | | end if | | | end for |
|