Abstract
An adaptive recursive stochastic output-only state space modeling approach is developed to improve the accuracy of modeling time-varying processes. The exponential weighted moving average approach is adopted to update the covariance and cross-covariance of past and future observation vectors. A novel method for adjusting forgetting factors based on the concept of angle between subspaces is proposed. To ensure stability of the identified model, we propose a constrained weighted recursive least square approach and propose a stable recursive canonical variate analysis (SRCVA) method. The performance of the proposed method is illustrated with simulation of the Tennessee Eastman (TE) process. Simulation results indicate that the accuracy of proposed SRCVA modeling method is superior to that of stochastic output-only state space modeling with canonical variate analysis.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: 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.