Share
Discrete-Time Neural Observers: Analysis and Applications (in English)
Alma Y. Alanis; Edgar N. Sanchez (Author)
·
Academic Press
· Paperback
Discrete-Time Neural Observers: Analysis and Applications (in English) - Alma Y. Alanis; Edgar N. Sanchez
$ 137.85
$ 229.75
You save: $ 91.90
Choose the list to add your product or create one New List
✓ Product added successfully to the Wishlist.
Go to My Wishlists
Origin: United Kingdom
(Import costs included in the price)
It will be shipped from our warehouse between
Wednesday, July 10 and
Monday, July 22.
You will receive it anywhere in United States between 1 and 3 business days after shipment.
Synopsis "Discrete-Time Neural Observers: Analysis and Applications (in English)"
Discrete-Time Neural Observers: Analysis and Applications presents recent advances in the theory of neural state estimation for discrete-time unknown nonlinear systems with multiple inputs and outputs. The book includes rigorous mathematical analyses, based on the Lyapunov approach, that guarantee their properties. In addition, for each chapter, simulation results are included to verify the successful performance of the corresponding proposed schemes. In order to complete the treatment of these schemes, the authors also present simulation and experimental results related to their application in meaningful areas, such as electric three phase induction motors and anaerobic process, which show the applicability of such designs. The proposed schemes can be employed for different applications beyond those presented. The book presents solutions for the state estimation problem of unknown nonlinear systems based on two schemes. For the first one, a full state estimation problem is considered; the second one considers the reduced order case with, and without, the presence of unknown delays. Both schemes are developed in discrete-time using recurrent high order neural networks in order to design the neural observers, and the online training of the respective neural networks is performed by Kalman Filtering. Presents online learning for Recurrent High Order Neural Networks (RHONN) using the Extended Kalman Filter (EKF) algorithmContains full and reduced order neural observers for discrete-time unknown nonlinear systems, with and without delaysIncludes rigorous analyses of the proposed schemes, including the nonlinear system, the respective observer, and the Kalman filter learningCovers real-time implementation and simulation results for all the proposed schemes to meaningful applications
- 0% (0)
- 0% (0)
- 0% (0)
- 0% (0)
- 0% (0)
All books in our catalog are Original.
The book is written in English.
The binding of this edition is Paperback.
✓ Producto agregado correctamente al carro, Ir a Pagar.
