Abstract
An independent neural network modeling of class analogy (INMCA) has been proposed as a classification pattern recognition method, which combines the idea of the classical soft independent modeling of class analogy (SIMCA) with the back-propagation neural network (BPN). The INMCA can not only exclude noise samples and select useful features in the multivariate calibration of complicated chemical processes, but also provide the class centers in the non-linear space for optimization of a chemical process. The data processing of a silicon steel process, as an application example, shows this INMCA to be useful.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
