Abstract
This study proposes an approach to the construction of granular models directly based on information granules expressed both in input and output spaces. Associating these information granules, the constructed granular models come in the framework of three layers networks: input granules, an inference scheme and output granules. The proposed approach consists of two stages. First, an augmented principle of justifiable granularity is proposed and applied to construct information granules in an input space. This principle constructs information granules not only through establishing a sound balance between two criteria, i.e., coverage and specificity, but also by optimizing those information granules on the basis of their homogeneity assessed with respect to data localized in output space. At the second stage, we propose an inference scheme by analyzing a location of an input datum in relation with the already formed information granules in an input space. The computed relation can be quantified as membership grades, thus yielding aggregation results involving information granules in an output space. The performance of the proposed granular model is supported by the mechanisms of granular computing and the principle of justifiable granularity. Experimental studies concerning synthetic and publicly available data are performed and some comparative analysis involving rule-based models is given.
Original language | English (US) |
---|---|
Article number | 108062 |
Journal | Applied Soft Computing |
Volume | 115 |
DOIs | |
State | Published - Jan 2022 |
All Science Journal Classification (ASJC) codes
- Software
Keywords
- Augmented principle of justifiable granularity
- Granular inference scheme
- Granular network
- Granular wrapper
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In: Applied Soft Computing, Vol. 115, 108062, 01.2022.
Research output: Contribution to journal › Article › peer-review
TY - JOUR
T1 - Granular models as networks of associations of information granules
T2 - A development scheme via augmented principle of justifiable granularity
AU - Jing, Tai Long
AU - Wang, Cong
AU - Pedrycz, Witold
AU - Li, Zhi Wu
AU - Succi, Giancarlo
AU - Zhou, Meng Chu
N1 - Funding Information: ZhiWu Li received the B.S. degree in mechanical engineering, the M.S. degree in automatic control, and the Ph.D. degree in manufacturing engineering from Xidian University, Xi’an, China, in 1989, 1992, and 1995, respectively. He joined Xidian University in 1992. He was a Visiting Professor with the University of Toronto, Toronto, ON, Canada; the Technion—Israel Institute of Technology, Haifa, Israel; the Martin-Luther University of Halle–Wittenburg, Halle, Germany; the Conservatoire National des Arts et Métiers, Paris, France; and Meliksah Universitesi, Kayseri, Turkey. He is currently with the Institute of Systems Engineering, Macau University of Science and Technology, Macau, China. His current research interests include Petri net theory and applications, supervisory control of discrete-event systems, workflow modeling and analysis, system reconfiguration, game theory, and data and process mining. Dr. Li was a recipient of the Alexander von Humboldt Research Grant and the Alexander von Humboldt Foundation, Germany. He is listed in Marquis Who’s Who in the World (27th ed., 2010). He serves as a Frequent Reviewer for more than 70 international journals, including Automatica and a number of the IEEE Transactions as well as many international conferences. He is the Founding Chair of the Xi’an Chapter of the IEEE Systems, Man, and Cybernetics Society. He is a member of Discrete-Event Systems Technical Committee of the IEEE Systems, Man, and Cybernetics Society, and was on the IFAC Technical Committee on Discrete-Event and Hybrid Systems, from 2011 to 2014. He is a Fellow of IEEE. Funding Information: This work was supported in part by the National Key R&D Project of China under Grant No. 2018YFB1700104 , in part by the National Natural Science Foundation of China under Grants Nos. 61873342 , 62076189 , in part by the China National Postdoctoral Program for Innovative Talents under Grant No. BX2021249 , in part by the fellowship of China Postdoctoral Science Foundation under Grant No. 2021M702678 , in part by the Recruitment Program of Global Experts, Canada Research Chair , in part by the Natural Sciences and Engineering Research Council of Canada , in part by the Science and Technology Development Fund, MSAR , under Grant No. 0012/2019/A1 , in part by the National Natural Science Foundation of China , under Grant No. 62076182 (W. Pedrycz), in part by the FDCT (Fundo para o Desenvolvimento das Ciencias e da Tecnologia) under Grant No. 0047/2021/A1 , and in part by the Ministry of Science and Higher Education of the Russian Federation as part of World-class Research Center program: Advanced Digital Technologies under Contract No. 075-15-2020-903 . Funding Information: MengChu Zhou received the B.S. degree in control engineering from Nanjing University of Science and Technology, Nanjing, China in 1983, M.S. degree in automatic control from Beijing Institute of Technology, Beijing, China in 1986, and Ph.D. degree in computer and systems engineering from Rensselaer Polytechnic Institute, Troy, NY, USA in 1990. He joined New Jersey Institute of Technology (NJIT), Newark, NJ, in 1990, and is now a Distinguished Professor of Electrical and Computer Engineering. His research interests are in Petri nets, intelligent automation, Internet of Things, big data, web services, and intelligent transportation. He has over 900 publications including 12 books, 600+ journal papers (500+ in IEEE Transactions, 29 patents and 29 book-chapters. He is the founding Editor of IEEE Press Book Series on Systems Science and Engineering, Editor-in-Chief of IEEE/CAA Journal of Automatica Sinica, and Associate Editor of IEEE Internet of Things Journal, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, and Frontiers of Information Technology & Electronic Engineering. He served as Associate Editor of IEEE Transactions on Robotics and Automation, IEEE Transactions on Automation Science and Engineering, and IEEE Transactions on Industrial Informatics, and Editor of IEEE Transactions on Automation Science and Engineering. He served as a Guest-Editor for many journals including IEEE Internet of Things Journal, IEEE Transactions on Industrial Electronics, and IEEE Transactions on Semiconductor Manufacturing. He is founding Chair/Co-chair of Technical Committee on AI-based Smart Manufacturing Systems of IEEE Systems, Man, and Cybernetics Society, Technical Committee on Semiconductor Manufacturing Automation and Technical Committee on Digital Manufacturing and Human-Centered Automation of IEEE Robotics and Automation Society. He was General Chair of IEEE Conf. on Automation Science and Engineering, Washington D.C., August 23-26, 2008, General Co-Chair of 2003 IEEE International Conference on System, Man and Cybernetics (SMC), Washington DC, October 5-8, 2003 and 2019 IEEE International Conference on SMC, Bari, Italy, Oct. 6-9, 2019, Founding General Co-Chair of 2004 IEEE Int. Conf. on Networking, Sensing and Control, Taipei, March 21-23, 2004, and General Chair of 2006 IEEE Int. Conf. on Networking, Sensing and Control, Ft. Lauderdale, Florida, U.S.A. April 23-25, 2006. He was Program Chair of 2010 IEEE International Conference on Mechatronics and Automation, August 4-7, 2010, Xi’an, China, 1998 and 2001 IEEE International Conference on SMC and 1997 IEEE International Conference on Emerging Technologies and Factory Automation. Dr. Zhou has led or participated in over 50 research and education projects with total budget over $12M, funded by National Science Foundation, Department of Defense, NIST, New Jersey Science and Technology Commission, and industry. He is a recipient of Excellence in Research Prize and Medal from NJIT, Humboldt Research Award for US Senior Scientists from Alexander von Humboldt Foundation, and Franklin V. Taylor Memorial Award and the Norbert Wiener Award from IEEE SMC Society, Computer-Integrated Manufacturing UNIVERSITY-LEAD Award from Society of Manufacturing Engineers, and Edison Patent Award from the Research & Development Council of New Jersey. He has been among most highly cited scholars since 2012 and ranked top one in the field of engineering worldwide in 2012 by Web of Science. He is a life member of Chinese Association for Science and Technology-USA and served as its President in 1999. He is a Fellow of IEEE, International Federation of Automatic Control (IFAC), American Association for the Advancement of Science (AAAS) and Chinese Association of Automation (CAA). Publisher Copyright: © 2021
PY - 2022/1
Y1 - 2022/1
N2 - This study proposes an approach to the construction of granular models directly based on information granules expressed both in input and output spaces. Associating these information granules, the constructed granular models come in the framework of three layers networks: input granules, an inference scheme and output granules. The proposed approach consists of two stages. First, an augmented principle of justifiable granularity is proposed and applied to construct information granules in an input space. This principle constructs information granules not only through establishing a sound balance between two criteria, i.e., coverage and specificity, but also by optimizing those information granules on the basis of their homogeneity assessed with respect to data localized in output space. At the second stage, we propose an inference scheme by analyzing a location of an input datum in relation with the already formed information granules in an input space. The computed relation can be quantified as membership grades, thus yielding aggregation results involving information granules in an output space. The performance of the proposed granular model is supported by the mechanisms of granular computing and the principle of justifiable granularity. Experimental studies concerning synthetic and publicly available data are performed and some comparative analysis involving rule-based models is given.
AB - This study proposes an approach to the construction of granular models directly based on information granules expressed both in input and output spaces. Associating these information granules, the constructed granular models come in the framework of three layers networks: input granules, an inference scheme and output granules. The proposed approach consists of two stages. First, an augmented principle of justifiable granularity is proposed and applied to construct information granules in an input space. This principle constructs information granules not only through establishing a sound balance between two criteria, i.e., coverage and specificity, but also by optimizing those information granules on the basis of their homogeneity assessed with respect to data localized in output space. At the second stage, we propose an inference scheme by analyzing a location of an input datum in relation with the already formed information granules in an input space. The computed relation can be quantified as membership grades, thus yielding aggregation results involving information granules in an output space. The performance of the proposed granular model is supported by the mechanisms of granular computing and the principle of justifiable granularity. Experimental studies concerning synthetic and publicly available data are performed and some comparative analysis involving rule-based models is given.
KW - Augmented principle of justifiable granularity
KW - Granular inference scheme
KW - Granular network
KW - Granular wrapper
UR - http://www.scopus.com/inward/record.url?scp=85121149654&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85121149654&partnerID=8YFLogxK
U2 - 10.1016/j.asoc.2021.108062
DO - 10.1016/j.asoc.2021.108062
M3 - Article
AN - SCOPUS:85121149654
SN - 1568-4946
VL - 115
JO - Applied Soft Computing
JF - Applied Soft Computing
M1 - 108062
ER -