Extended dissipativity of generalised neural networks including time delays

ABSTRACT

This article explores the extended dissipativity conditions for generalised neural networks (GNNs) including interval time-varying delays. Extended dissipativity criterions are proposed by making proper Lyapunov–Krasovskii functional. The improved reciprocally convex combination and weighted integral inequality techniques are together applied in main results to establish the new extended dissipativity conditions of delayed GNNs. Finally, the feasibility and superiority of the proposed novel approach is clearly shown by numerical examples.

KEYWORDS:

• Generalised neural network
• extended dissipativity analysis
• time-varying delay
• weighted integral inequality

Acknowledgment

This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1A6A1A03013567) and by the Korea government (MEST) (NRF-2015R1A2A2A05001610) and in part by the Thailand Research Fund (TRF), Thailand.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

National Research Foundation of Korea [grant number NRF-2016R1A6A1A03013567], [grant number NRF-2015R1A2A2A05001610]; Thailand Research Fund (TRF).

Notes on contributors

R. Saravanakumar

R. Saravanakumar was born in 1989. He under graduated in the field of Mathematics during 2006–2009 from Sri Ramakrishna Mission Vidyalaya College of Arts and Science affiliated to Bharathiar University, Coimbatore, Tamil Nadu, India. He postgraduated in Mathematics from Gandhigram Rural University, Gandhigram, Tamil Nadu, India, during 2010–2012. He received Doctor of Philosophy in Mathematics from Thiruvalluvar University, Tamil Nadu, India in 2016. He was availed Junior Research Fellowship during 2012-2015 from National Board for Higher Mathematics (DAE), Government of India, Mumbai. Currently he is working as a Post-doctoral research fellow in Research centre for wind energy systems, Kunsan National University, Gunsan, South Korea. He serves as a reviewer for various SCI journals. He has authored and co-authored of more than 20 research articles in various SCI journals holding impact factors. His research interests include robust H∞ control, wind turbine control, networked control systems, state estimation, neural networks, stochastic, Markovian Jump and memristor systems.

Grienggrai Rajchakit

Grienggrai Rajchakitreceived his M.Sc. degree in Mathematics at the Chiang Mai University, Thailand in 2001 and Ph.D. degree at the King Mongkut University, Thailand in 2007, respectively. He is the author/co-author of 61 refereed journal papers. In 2007 he joined the Department of Mathematics, Maejo University Thailand as a Lecturer, where he became an Assistant Professor, in 2013. His research areas include mathematical analysis, stability analysis, neural networks and difference equations

M. Syed Ali

M. Syed Ali graduated from the Department of Mathematics of Gobi Arts and Science College affiliated to Bharathiar University, Coimbatore in 2002. He received his post-graduation in Mathematics from Sri Ramakrishna Mission Vidyalaya College of Arts and Science affiliated to Bharathiar University, Coimbatore, Tamil Nadu, India, in 2005. He was awarded Master of Philosophy in 2006 in the field of Mathematics with specialized area of Numerical Analysis from Gandhigram Rural University Gandhigram, India. He was conferred with Doctor of Philosophy in 2010 in the field of Mathematics specialized in the area of Fuzzy Neural Networks in Gandhigram Rural University, Gandhigram, India. He was selected as a Post-Doctoral Fellow in the year 2010 for promoting his research in the field of Mathematics at Bharathidasan University, Trichy, Tamil Nadu and also worked there from November 2010 to February 2011. Since 2011 he is working as an Assistant Professor in Department of Mathematics, Thiruvalluvar University, Vellore, Tamil Nadu, India. He has received Young Scientist Award from The Academy of Sciences, Chennai, India. He has published more than 60 research papers in various SCI journals holding impact factors. He has also published research articles in national journals and international conference proceedings. He also serves as a reviewer for few SCI journals. His research interests include stochastic differential equations, dynamical systems, fuzzy neural networks, complex networks and cryptography.

Young Hoon Joo

Young Hoon Joo received his B.S., M.S., and Ph.D. degrees in Electrical Engineering from Yonsei University, Seoul, Korea, in 1982, 1984, and 1995, respectively. He worked with Samsung Electronics Company, Seoul, Korea, from 1986 to 1995, as a project manager. He was with the University of Houston, Houston, TX, from 1998 to 1999, as a visiting professor in the Department of Electrical and Computer Engineering. He is currently a professor in the Department of Control and Robotics Engineering, Kunsan National University, Korea. His major interest is mainly in the field of intelligent control, intelligent robot, human-robot interaction, wind-farm control, power system stabilization, and intelligent surveillance systems. He served as President for Korea Institute of Intelligent Systems (KIIS) (2008-2009) and the Vice-President for the Korean Institute of Electrical Engineers (KIEE) (2013-2016) and is serving as the Editor-in-Chief for the International Journal of Control, Automation, and Systems (IJCAS) (2014-present) and Vice-President for Institute of Control, Automation, and Systems (ICROS) (2016-present). Also, he is serving as Director of Research Centre of Wind Energy Systems funded by Korean Government (2016-present).