Neural Network-Based State Estimation of Nonlinear Systems

"Neural Network-Based State Estimation of Nonlinear Systems" presents efficient, easy to implement neural network schemes for state estimation, system identification, and fault detection and Isolation with mathematical proof of stability, experimental evaluation, and Robustness against unmolded dynamics, external disturbances, and measurement noises.

This series aims to report new developments in the fields of control and information sciences –quickly, informally and at a high level. The type material considered for publication includes:

1. Preliminary drafts of monographs and advanced textbooks

2. Lectures on a new field, or presenting a new angle on a classical field

3. Research reports

4. Reports of meetings, provided they are a) of exceptional interest and b) devoted to a specific topic. The timeliness of subject material is very important.

Information for Authors

Manuscripts should be written in English and be no less the 100, preferably no more than 500 pages. The manuscript in its final and approved version must be submitted in camera-ready form. Authors are encouraged to use LATEX together with the corresponding Springer LATEX macro packages. The corresponding electronic files are also required for the production process, in paticular the online version. Detailed instructions for authors can be found on the engineering site of our homepage: springer.com/series/642. Manuscripts should be sent to one of the series editors, Professor Dr.-Ing. M. Thomas, Institut für Regelungstechnik, Technische Universität, Appelstraße 11, 30167 Hannover, Germany, or Professor M. Morari, Institut für Automatik, ETH/ETL I 29, Physikstraße 3, 8092 Zürich, Switzerland, or directly to the Engineering Editor, Springer-Verlag, Tiergartenstresße 17, 69121 Heidelberg, Germany.