Stability and Synchronization Control of Stochastic Neural Networks
This book reports on the latest findings in the study of Stochastic Neural Networks (SNN). The book collects the novel model of the disturbance driven by Levy process, the research method of M-matrix, and the adaptive control method of the SNN in the
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Wuneng Zhou Jun Yang Liuwei Zhou Dongbing Tong
Stability and Synchronization Control of Stochastic Neural Networks
Studies in Systems, Decision and Control Volume 35
Series editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland e-mail: [email protected]
About this Series The series “Studies in Systems, Decision and Control” (SSDC) covers both new developments and advances, as well as the state of the art, in the various areas of broadly perceived systems, decision making and control- quickly, up to date and with a high quality. The intent is to cover the theory, applications, and perspectives on the state of the art and future developments relevant to systems, decision making, control, complex processes and related areas, as embedded in the fields of engineering, computer science, physics, economics, social and life sciences, as well as the paradigms and methodologies behind them. The series contains monographs, textbooks, lecture notes and edited volumes in systems, decision making and control spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems, Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems, Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution and exposure which enable both a wide and rapid dissemination of research output.
More information about this series at http://www.springer.com/series/13304
Wuneng Zhou Jun Yang Liuwei Zhou Dongbing Tong •
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Stability and Synchronization Control of Stochastic Neural Networks
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Wuneng Zhou School of Information Sciences and Technology Donghua University Shanghai China
Liuwei Zhou School of Information Sciences and Technology Donghua University Shanghai China
Jun Yang Anyang Normal University Anyang China
Dongbing Tong Shanghai University of Engineering Science Shanghai China
ISSN 2198-4182 ISSN 2198-4190 (electronic) Studies in Systems, Decision and Control ISBN 978-3-662-47832-5 ISBN 978-3-662-47833-2 (eBook) DOI 10.1007/978-3-662-47833-2 Library of Congress Control Number: 2015946075 Springer Heidelberg New York Dordrecht London © Springer-Verlag Berlin Heidelberg 2016 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from
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