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Finite- Time Stabilization of Nontriangular Systems with Full State Constraints Conference paper
2019 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2019
Authors:  Sui,Shuai;  Chen,C. L.Philip;  Feng,Shuang
Favorite |  | TC[WOS]:0 TC[Scopus]:0 | Submit date:2021/03/09
backstepping  Barrier Lyapunov functions  finite-time stability  full state constraints  nontriangular systems  
Adaptive Control of Vehicle Yaw Rate with Active Steering System and Extreme Learning Machine Conference paper
Proceedings of ELM, Yatai, China, 4-7 October
Authors:  Pak Kin Wong;  Wei Huang;  Ka In Wong;  Chi Man Vong
Favorite |  | TC[WOS]:0 TC[Scopus]:0 | Submit date:2019/04/11
Adaptive Elm Control  Active Steering  Vehicle Yaw Rate Control  
Actuator Failure Compensation Control for Stochastic Nonlinear Systems Conference paper
Authors:  Wang, Jianhui;  Liu, Zhi;  Zhang, Yun;  Chen, C. L. Philip;  IEEE
Favorite |  | TC[WOS]:1 TC[Scopus]:0 | Submit date:2018/10/30
Actuator Failure/fault  Adaptive Control  Stochastic Nonlinear Systems  Fuzzy control  
A fuzzy logic based control algorithm for EV suspension systems Conference paper
EVS 2016 - 29th International Electric Vehicle Symposium, Montreal, QC, Canada, 6 19, 2016 - 6 22, 2016
Authors:  Huang, Shaojia;  Ching, T.W.;  Li, Wenlong;  Zeng, Luwei;  Chau, K.T.;  Chan, C.C.
Favorite |  | TC[WOS]:0 TC[Scopus]:0 | Submit date:2018/11/06
A new fast-F-CONFIS training of fully-connected neuro-fuzzy inference system Conference paper
ICCSS 2015 - Proceedings: 2015 International Conference on Informative and Cybernetics for Computational Social Systems, Chengdu, PEOPLES R CHINA, AUG 13-15, 2015
Authors:  Jing Wang;  Yuan-Yan Tang;  Long Chen;  C. L. Philip Chen;  Chao-Tian Chen
Favorite |  | TC[WOS]:0 TC[Scopus]:0 | Submit date:2019/02/11
Conjugate Gradients  Fuzzy Logic  Fuzzy Neural Networks  Gradient Descent  Neural Networks  
Mixed radix systems of fully connected neuro-fuzzy inference systems with special properties Conference paper
ICCSS 2015 - Proceedings: 2015 International Conference on Informative and Cybernetics for Computational Social Systems
Authors:  Wang J.;  Chen C.-T.;  Chen C.L.P.;  Yu Y.-Q.
Favorite |  | TC[WOS]:0 TC[Scopus]:0 | Submit date:2019/02/11
Fully Connected Neuro-Fuzzy System  Fuzzy Logic  Fuzzy Neural Networks  Gradient Descent  Neural Networks  Neuro-Fuzzy System  
Novel-fuzzy-model based modeling and control of nonlinear chaotic systems with uncertainty Conference paper
2015 International Conference on Advanced Robotics and Intelligent Systems, ARIS 2015
Authors:  Tam L.-M.;  Li S.-Y.
Favorite |  | TC[WOS]:0 TC[Scopus]:1 | Submit date:2019/02/13
Chaotic system with uncertainty  Fuzzy control  Novel fuzzy model  
Decentralized control for second-order uncertain nonlinear multi-agent systems consensus problem based on fuzzy adaptive high-gain observer Conference paper
Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013
Authors:  Ren C.;  Philip Chen C.L.
Favorite |  | TC[WOS]:10 TC[Scopus]:10 | Submit date:2019/02/11
Adaptive decentralized control  Consensus control  High-gain state observer  Multi-agent systems  Nonlinear unknown dynamics  
System modeling and synchronization of nonlinear chaotic systems with uncertainty and disturbance by innovative fuzzy modeling strategy Conference paper
2013 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ - IEEE 2013), Hyderabad, INDIA, JUL 07-10, 2013
Authors:  Li S.-Y.;  Ko L.-W.;  Lin C.-T.;  Tam L.-M.;  Chen H.-K.;  Lao S.-K.
Favorite |  | TC[WOS]:0 TC[Scopus]:1 | Submit date:2019/02/13
Fuzz Logic  Ge-li Fuzzy Model  Linear Matrix Inequality (Lmi)  
Finding the near optimal learning rates of Fuzzy Neural Networks (FNNs) via its equivalent fully connected neural networks (FFNNs) Conference paper
Proceedings 2012 International Conference on System Science and Engineering, ICSSE 2012
Authors:  Wang J.;  Chen C.L.P.;  Wang C.-H.
Favorite |  | TC[WOS]:0 TC[Scopus]:1 | Submit date:2019/02/11
Back Propagations  Fuzzy Logic  Fuzzy Neural Networks  Gradient Descent  Neural Networks  Optimal training