Stability analysis in identification of interval type-2 adaptive neuro-fuzzy inference system: Contribution to a novel Lyapunov function
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[1] Mehmet Konar,et al. Comparison of Sugeno and Mamdani fuzzy models optimized by artificial bee colony algorithm for nonlinear system modelling , 2016 .
[2] Mojtaba Ahmadieh Khanesar,et al. Identification of Nonlinear Dynamic Systems Using Type-2 Fuzzy Neural Networks—A Novel Learning Algorithm and a Comparative Study , 2015, IEEE Transactions on Industrial Electronics.
[3] Khaled Almejalli,et al. GA-based learning for rule identification in fuzzy neural networks , 2015, Appl. Soft Comput..
[4] Mohammad Teshnehlab,et al. Identification using ANFIS with intelligent hybrid stable learning algorithm approaches , 2009, Neural Computing and Applications.
[5] Azlan Mohd Zain,et al. Robust optimization of ANFIS based on a new modified GA , 2015, Neurocomputing.
[6] Mohammad Teshnehlab,et al. Training ANFIS as an identifier with intelligent hybrid stable learning algorithm based on particle swarm optimization and extended Kalman filter , 2009, Fuzzy Sets Syst..
[7] Alireza Alfi,et al. Adaptive parameter control of search group algorithm using fuzzy logic applied to networked control systems , 2018, Soft Comput..
[8] Mojtaba Ahmadieh Khanesar,et al. Optimal design of adaptive type-2 neuro-fuzzy systems: A review , 2016, Appl. Soft Comput..
[9] Mojtaba Ahmadieh Khanesar,et al. Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods , 2009, Appl. Soft Comput..
[10] Jerry M. Mendel,et al. Interval Type-2 Fuzzy Logic Systems Made Simple , 2006, IEEE Transactions on Fuzzy Systems.
[11] Jerry M. Mendel,et al. Type-2 fuzzy sets made simple , 2002, IEEE Trans. Fuzzy Syst..
[12] M. Teshnehlab,et al. Training ANFIS structure with modified PSO algorithm , 2007, 2007 Mediterranean Conference on Control & Automation.
[13] Jerry M. Mendel,et al. Centroid of a type-2 fuzzy set , 2001, Inf. Sci..
[14] Alfonso García-Cerezo,et al. Object-oriented approach applied to ANFIS modeling and control of a distillation column , 2013, Expert Syst. Appl..
[15] Chia-Feng Juang,et al. Reinforcement Interval Type-2 Fuzzy Controller Design by Online Rule Generation and Q-Value-Aided Ant Colony Optimization , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[16] Mojtaba Ahmadieh Khanesar,et al. Extended Kalman Filter Based Learning Algorithm for Type-2 Fuzzy Logic Systems and Its Experimental Evaluation , 2012, IEEE Transactions on Industrial Electronics.
[17] Besir Dandil,et al. Design and implementation of Type-2 fuzzy neural system controller for PWM rectifiers , 2017 .
[18] Patricia Melin,et al. Particle swarm optimization of interval type-2 fuzzy systems for FPGA applications , 2013, Appl. Soft Comput..
[19] Minghao Chen,et al. Constructing optimized interval type-2 TSK neuro-fuzzy systems with noise reduction property by quantum inspired BFA , 2016, Neurocomputing.
[20] Peilin Liu,et al. Training ANFIS Model with an Improved Quantum-Behaved Particle Swarm Optimization Algorithm , 2013 .
[21] Hicham Chaoui,et al. Adaptive Interval Type-2 Fuzzy Logic Control for PMSM Drives With a Modified Reference Frame , 2017, IEEE Transactions on Industrial Electronics.
[22] Sebastian Ruder,et al. An overview of gradient descent optimization algorithms , 2016, Vestnik komp'iuternykh i informatsionnykh tekhnologii.
[23] Okyay Kaynak,et al. Type 2 Fuzzy Neural Structure for Identification and Control of Time-Varying Plants , 2010, IEEE Transactions on Industrial Electronics.
[24] Ching-Chih Tsai,et al. Generalized predictive control using recurrent fuzzy neural networks for industrial processes , 2007 .
[25] Mojtaba Ahmadieh Khanesar,et al. Levenberg marquardt algorithm for the training of type-2 fuzzy neuro systems with a novel type-2 fuzzy membership function , 2011, 2011 IEEE Symposium on Advances in Type-2 Fuzzy Logic Systems (T2FUZZ).
[26] José Manuel Andújar Márquez,et al. A general methodology for online TS fuzzy modeling by the extended Kalman filter , 2014, Appl. Soft Comput..
[27] Mojtaba Ahmadieh Khanesar,et al. Levenberg-Marquardt training method for Type-2 fuzzy neural networks and its stability analysis , 2015, 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).
[28] George W. Irwin,et al. A New Gradient Descent Approach for Local Learning of Fuzzy Neural Models , 2013, IEEE Transactions on Fuzzy Systems.
[29] Robert Ivor John,et al. Learning of interval and general type-2 fuzzy logic systems using simulated annealing: Theory and practice , 2016, Inf. Sci..
[30] Robert Ivor John,et al. Time series forecasting using a TSK fuzzy system tuned with simulated annealing , 2010, International Conference on Fuzzy Systems.
[31] Dervis Karaboga,et al. Self-generated fuzzy systems design using artificial bee colony optimization , 2015, Inf. Sci..
[32] Shaoyuan Li,et al. Interval type-2 fuzzy T-S modeling for a heat exchange process on CE117 Process Trainer , 2011, Proceedings of 2011 International Conference on Modelling, Identification and Control.
[33] Ali Sadollah,et al. Gradient-based Water Cycle Algorithm with evaporation rate applied to chaos suppression , 2017, Appl. Soft Comput..
[34] Sedat Bayseç,et al. Nonlinear identification of a spark ignition engine torque based on ANFIS with NARX method , 2016, Expert Syst. J. Knowl. Eng..
[35] Chin-Teng Lin,et al. A TSK-Type-Based Self-Evolving Compensatory Interval Type-2 Fuzzy Neural Network (TSCIT2FNN) and Its Applications , 2014, IEEE Transactions on Industrial Electronics.
[36] E. Mizutani,et al. Levenberg-Marquardt method for ANFIS learning , 1996, Proceedings of North American Fuzzy Information Processing.
[37] Jerry M. Mendel,et al. Stability analysis of type-2 fuzzy systems , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).
[38] Mohammad Bagher Menhaj,et al. Stability analysis of recurrent type-2 TSK fuzzy systems with nonlinear consequent part , 2015, Neural Computing and Applications.
[39] Gerardo M. Mendez,et al. Hybrid learning mechanism for interval A2-C1 type-2 non-singleton type-2 Takagi-Sugeno-Kang fuzzy logic systems , 2013, Inf. Sci..
[40] Mohammad Bagher Menhaj,et al. Stable ANFIS2 for Nonlinear System Identification , 2016, Neurocomputing.
[41] Mohammad Teshnehlab,et al. Novel Hybrid Learning Algorithms for Tuning ANFIS Parameters as an Identifier Using Fuzzy PSO , 2008, 2008 IEEE International Conference on Networking, Sensing and Control.
[42] Gerardo M. Mendez,et al. Type-1 Non-singleton Type-2 Takagi-Sugeno-Kang Fuzzy Logic Systems Using the Hybrid Mechanism Composed by a Kalman Type Filter and Back Propagation Methods , 2010, HAIS.
[43] Chia-Feng Juang,et al. Rule-Based Cooperative Continuous Ant Colony Optimization to Improve the Accuracy of Fuzzy System Design , 2014, IEEE Transactions on Fuzzy Systems.
[44] Hung-Suck Park,et al. Comparative analysis on the application of neuro-fuzzy models for complex engineered systems: Case study from a landfill and a boiler , 2017, Expert Syst. J. Knowl. Eng..
[46] Jerry M. Mendel,et al. Equalization of nonlinear time-varying channels using type-2 fuzzy adaptive filters , 2000, IEEE Trans. Fuzzy Syst..
[47] Chin-Teng Lin,et al. Neural-Network-Based Fuzzy Logic Control and Decision System , 1991, IEEE Trans. Computers.