Fr. 47.50

MOR applications

English, German · Paperback / Softback

Shipping usually within 2 to 3 weeks (title will be printed to order)

Description

Read more

Model order reduction is a technique for reducing the computational complexity of mathematical models in numerical simulations. In recent years, model reduction has become an omnipresent tool in a variety of application regions and, consequently, a research emphasis for many mathematicians and engineers. In the present book mixed methods are employed for order reduction. The denominator polynomial of reduced model is obtained by using modified pole clustering, stability equation method and Routh method. The numerator is obtained by factor division method and Pade approximation. Five Mixed methods Pade approximation-Modified pole clustering, Factor division method, Stability equation method, Pade approximation, Routh method, Factor division method-Routh method and Pade approximation-Stability equation method have been used for the reduction of 8th order Higher order system to second order reduced models.

About the author

P Verma is an active researcher in the area of Control system. Her area of expertise is MOR and its techniques. She has done B.Tech. and presently pursuing M.Tech. in control system.Dr. P K Juneja, PhD, IITR, is Professor at GEU Dehradun.Mr. M Chaturvedi, M.Tech., control systems is Assistant Professor at GEU Dehradun.

Product details

Authors M Chaturvedi, M. Chaturvedi, P Juneja, P K Juneja, Verma, P Verma, P. Verma
Publisher LAP Lambert Academic Publishing
 
Languages English, German
Product format Paperback / Softback
Released 16.08.2016
 
EAN 9783659927195
ISBN 978-3-659-92719-5
No. of pages 60
Subject Natural sciences, medicine, IT, technology > Technology

Customer reviews

No reviews have been written for this item yet. Write the first review and be helpful to other users when they decide on a purchase.

Write a review

Thumbs up or thumbs down? Write your own review.

For messages to CeDe.ch please use the contact form.

The input fields marked * are obligatory

By submitting this form you agree to our data privacy statement.