Open access peer-reviewed Edited Volume

Dynamic Data Assimilation

Beating the Uncertainties

Edited by Dinesh G. Harkut

Prof Ram Mehge College of Engineering and Management

Data assimilation is a process of fusing data with a model for the singular purpose of estimating unknown variables. It can be used, for example, to predict the evolution of the atmosphere at a given point and time. This book examines data assimilation methods including Kalman filtering, artificial intelligence, neural networks, machine learning, and cognitive computing.

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Dynamic Data AssimilationBeating the UncertaintiesEdited by Dinesh G. Harkut

Published: October 28th 2020

DOI: 10.5772/intechopen.87789

ISBN: 978-1-83968-084-7

Print ISBN: 978-1-83968-083-0

eBook (PDF) ISBN: 978-1-83968-085-4

Copyright year: 2020

Books open for chapter submissions

2461 Total Chapter Downloads

2 Crossref Citations

1 Web of Science Citations

4 Dimensions Citations


Open access peer-reviewed

1. Introductory Chapter: Data Assimilation

By Dinesh G. Harkut


Open access peer-reviewed

2. Adaptive Filter as Efficient Tool for Data Assimilation under Uncertainties

By Hong Son Hoang and Remy Baraille


Open access peer-reviewed

3. Convolutional Neural Network Demystified for a Comprehensive Learning with Industrial Application

By Anand Raju and Shanthi Thirunavukkarasu


Open access peer-reviewed

4. Estimation for Motion in Tracking and Detection Objects with Kalman Filter

By Afef Salhi, Fahmi Ghozzi and Ahmed Fakhfakh


Open access peer-reviewed

5. Kalman Filtering Applied to Induction Motor State Estimation

By Yassine Zahraoui and Mohamed Akherraz


Open access peer-reviewed

6. Data Processing Using Artificial Neural Networks

By Wesam Salah Alaloul and Abdul Hannan Qureshi


Edited Volume and chapters are indexed in

  • Worldcat
  • OpenAIRE
  • Google Scholar
  • AZ ebsco
  • Base
  • CNKI

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