Abstract
In this paper, we proposed a new identification method for changing parameter system based on modular network SOM (mnSOM). mnSOM is a generalization of Self Organizing Maps (SOM) formed by replacing each vector unit of SOM with function module. The modular function could be a multi layer perceptron, a recurrent neural network or even SOM itself. For system identification purpose, we employed ARX modules with recursive least square (RLS) learning. Inherit SOM capability of interpolating among function modules and well-known powerful capability of least square methods for system identification our RLS-mnSOM provides promising results for identification of changing parameter systems.
Sign-in/Register to access full text options 
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.
