Nonparametric Identification of Nonlinear Dynamic Systems

By G�bor Kenderi

A nonparametric identification method for highly nonlinear systems is presented that is able to reconstruct the underlying nonlinearities without a priori knowledge of the describing nonlinear functions. The approach is based on nonlinear Kalman Filter algorithms using the well-known state augmentation technique that turns the filter into a dual state and parameter estimator, of which an extension towards nonparametric identification is proposed in the present work.

This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.

• Format: Paperback
• Language: English
• ISBN: 1013279301
• ISBN13: 9781013279300
• Release Date: October 2020
• Publisher: Saint Philip Street Press
• Length: 232 Pages
• Weight: 1.21 lbs.
• Dimensions: 0.5" x 8.5" x 11.0"