Abstract
A systematic identification procedure is described for multivariable local linear state-space models. Normalized radial basis functions are used as weighting functions and the system matrices of the local models are fully parameterized. An iterative projected gradient search method is used to deal with the nonuniqueness of the fully parameterized model. Scheduling the local models with the input or with the output is discussed. The choice of scheduling variable influences the stability of training the recurrent state-space model. The procedure is illustrated using data from the Silver-box case study.
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.
