Application of the Extended Kalman filter to fuzzy modeling: Algorithms and practical implementation

Piña, Antonio Javier Barragán; Andújar, José Manuel; Torres, Mariano J. Aznar; Avelló, Agustín Jiménez; Al‐Hadithi, Basil Mohammed

doi:10.2991/eusflat.2011.23

Cited by 2 publications

(1 citation statement)

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“…Motivated by the successful use of Kalman filter in the works presented above, and considering that this algorithm can be applied in real time (Jiménez et al 2008), a general methodology for use EKF to estimate the adaptive parameters of a general TS fuzzy model, was presented in (Barragán et al 2011a(Barragán et al , b, 2013. This methodology uses the excellent features of Kalman filter to obtain fuzzy models of unknown systems from input-output data.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Modeling and Control: Theory and Applications

Mata¹,

Marichal²,

Jimnez³

2014

Atlantis Computational Intelligence Systems

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Aims and scope of the seriesThe series 'Atlantis Computational Intelligence Systems' aims at covering state-of-the-art research and development in all fields where computational intelligence is investigated and applied. The series seeks to publish monographs and edited volumes on foundations and new developments in the field of computational intelligence, including fundamental and applied research as well as work describing new, emerging technologies originating from computational intelligence research. Applied CI research may range from CI applications in the industry to research projects in the life sciences, including research in biology, physics, chemistry and the neurosciences.All books in this series are co-published with Springer.For more information on this series and our other book series, please visit our website at: www.atlantis-press.com/publications/books. This book is published under the Creative Commons Attribution-Non-commercial license, meaning that copying, distribution, transmitting and adapting the book is permitted, provided that this is done for non-commercial purposes and that the book is attributed. This book, or any parts thereof, may not be reproduced for commercial purposes in any form or by any means, electronic or mechanical, including photocopying, recording or any information storage and retrieval system known or to be invented, without prior permission from the Publisher.Printed on acid-free paper ForewordAlmost half a century has passed since the publication of the seminal paper by Prof. Lotfi A. Zadeh. Fifty years that have witnessed an impressive growth of fuzzy systems from both the theoretical and applied points of view. Impressive growth and at the same time following an unforeseen way, since as Prof. Zadeh has said many times, his expectations when writing his first paper on fuzzy sets in 1965 were that fuzzy sets would find its main applications in the realm of humanistic systems (economics, linguistics, psychology, etc.). Now the reality is that most of the success of fuzzy systems research (at least in its initial decades) was related to its application to mechanistic instead of humanistic systems. This book concentrates on modeling and control of mechanistic systems, as can be seen when reading the Table of Contents, first considering important theoretical aspects and then showing its application to quite interesting real-world problems.Any book on fuzzy modeling and control is somehow a tribute to those who paved the way for the topic. In this case, we need to consider three key persons and three related milestones in the history of fuzzy sets. First, the already mentioned Prof. Zadeh and his 1973 paper ''Outline of a New Approach to the Analysis of Complex Systems and Decision Processes,'' IEEE Transactions on Systems, Man, and Cybernetics 3: 28-44 (1973), the paper where concepts as linguistic variable and fuzzy if-then rules took form, and at the same time, the paper that triggered the second hint in the road to build up the theory of fuzzy modeling and control. Ab...

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Section: Introductionmentioning

confidence: 99%