Stability analysis in identification of interval type‐2 adaptive neuro‐fuzzy inference system: Contribution to a novel Lyapunov function-Reference-Cited by-同舟云学术

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Stability analysis in identification of interval type‐2 adaptive neuro‐fuzzy inference system: Contribution to a novel Lyapunov function

Published:2019-07-28 Issue:6 Volume:36 Page:
ISSN:0266-4720
Container-title:Expert Systems
language:en
Short-container-title:Expert Systems

Author:

Zabihi Shesh Poli Mohammad Mahdi¹^ORCID,Aliyari Shoorehdeli Mahdi²^ORCID,Moarefianpour Ali¹

Affiliation:

1. Department of Electrical Engineering, Science and Research BranchIslamic Azad University Tehran Iran

2. Faculty of Electrical Engineering, Department of Mechatronics Engineering, Industrial Control Center of ExcellenceAPAC Research Group K. N. Toosi University of Technology Tehran Iran

Publisher

Wiley

Subject

Artificial Intelligence,Computational Theory and Mathematics,Theoretical Computer Science,Control and Systems Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/exsy.12457

Reference56 articles.

1. Type 2 Fuzzy Neural Structure for Identification and Control of Time-Varying Plants

2. Almaraashi M. John R. Coupland S. &Hopgood A.(2010 July).Time series forecasting using a TSK fuzzy system tuned with simulated annealing. InFuzzy Systems (FUZZ) 2010 IEEE International Conference on(pp.1‐6). IEEE.

3. Learning of interval and general type-2 fuzzy logic systems using simulated annealing: Theory and practice

4. Comparison of Sugeno and Mamdani fuzzy models optimized by artificial bee colony algorithm for nonlinear system modelling

5. A general methodology for online TS fuzzy modeling by the extended Kalman filter

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