Littérature scientifique sur le sujet « Resolution of fuzzy polynomial systems »
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Articles de revues sur le sujet "Resolution of fuzzy polynomial systems"
Adil, Bouhouch, Er-Rafyg Aicha et Ez-Zahout Abderrahmane. « Neural network to solve fuzzy constraint satisfaction problems ». IAES International Journal of Artificial Intelligence (IJ-AI) 13, no 1 (1 mars 2024) : 228. http://dx.doi.org/10.11591/ijai.v13.i1.pp228-235.
Texte intégralGerman, Oleg, et Sara Nasrh. « New Method for Optimal Feature Set Reduction ». Informatics and Automation 19, no 6 (11 décembre 2020) : 1198–221. http://dx.doi.org/10.15622/ia.2020.19.6.3.
Texte intégralChen, Ying-Jen, Hua O. Wang, Motoyasu Tanaka, Kazuo Tanaka et Hiroshi Ohtake. « Discrete polynomial fuzzy systems control ». IET Control Theory & ; Applications 8, no 4 (6 mars 2014) : 288–96. http://dx.doi.org/10.1049/iet-cta.2013.0645.
Texte intégralQiu, Yu, Hong Yang, Yan-Qing Zhang et Yichuan Zhao. « Polynomial regression interval-valued fuzzy systems ». Soft Computing 12, no 2 (23 mai 2007) : 137–45. http://dx.doi.org/10.1007/s00500-007-0189-4.
Texte intégralAubry, Philippe, Jérémy Marrez et Annick Valibouze. « Computing real solutions of fuzzy polynomial systems ». Fuzzy Sets and Systems 399 (novembre 2020) : 55–76. http://dx.doi.org/10.1016/j.fss.2020.01.004.
Texte intégralOH, S., W. PEDRYCZ et S. ROH. « Genetically optimized fuzzy polynomial neural networks with fuzzy set-based polynomial neurons ». Information Sciences 176, no 23 (4 décembre 2006) : 3490–519. http://dx.doi.org/10.1016/j.ins.2005.11.009.
Texte intégralKu, Cheung-Chieh, Chein-Chung Sun, Shao-Hao Jian et Wen-Jer Chang. « Passive Fuzzy Controller Design for the Parameter-Dependent Polynomial Fuzzy Model ». Mathematics 11, no 11 (28 mai 2023) : 2482. http://dx.doi.org/10.3390/math11112482.
Texte intégralKharrati, Hamed, Sohrab Khanmohammadi, Witold Pedrycz et Ghasem Alizadeh. « Improved Polynomial Fuzzy Modeling and Controller with Stability Analysis for Nonlinear Dynamical Systems ». Mathematical Problems in Engineering 2012 (2012) : 1–21. http://dx.doi.org/10.1155/2012/273631.
Texte intégralShen, Yu-Hsuan, Ying-Jen Chen, Fan-Nong Yu, Wen-June Wang et Kazuo Tanaka. « Descriptor Representation-Based Guaranteed Cost Control Design Methodology for Polynomial Fuzzy Systems ». Processes 10, no 9 (7 septembre 2022) : 1799. http://dx.doi.org/10.3390/pr10091799.
Texte intégralNasiri, Alireza, Sing Kiong Nguang, Akshya Swain et Dhafer Almakhles. « Stabilisation of discrete-time polynomial fuzzy systems via a polynomial lyapunov approach ». International Journal of Systems Science 49, no 3 (21 décembre 2017) : 557–66. http://dx.doi.org/10.1080/00207721.2017.1407006.
Texte intégralThèses sur le sujet "Resolution of fuzzy polynomial systems"
Xiao, Bo. « Stability and performance analysis of polynomial fuzzy-model-based control systems and interval type-2 fuzzy logic systems ». Thesis, King's College London (University of London), 2018. https://kclpure.kcl.ac.uk/portal/en/theses/stability-and-performance-analysis-of-polynomial-fuzzymodelbased-control-systems-and-interval-type2-fuzzy-logic-systems(1a455ca8-f27d-49aa-ab4a-8ae697aeba17).html.
Texte intégralMarrez, Jérémy. « Représentations adaptées à l'arithmétique modulaire et à la résolution de systèmes flous ». Electronic Thesis or Diss., Sorbonne université, 2019. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2019SORUS635.pdf.
Texte intégralModular computations involved in public key cryptography applications most often use a standardized prime modulo, the choice of which is not always free in practice. The improvement of modular operations is fundamental for the efficiency and safety of these primitives. This thesis proposes to provide an efficient modular arithmetic for the largest possible number of primes, while protecting it against certain types of attacks. For this purpose, we are interested in the PMNS system used for modular arithmetic, and propose methods to obtain many PMNS for a given prime, with an efficient arithmetic on the representations. We also consider the randomization of modular computations via algorithms of type Montgomery and Babaï by exploiting the intrinsic redundancy of PMNS. Induced changes of data representation during the calculation prevent an attacker from making useful assumptions about these representations. We then present a hybrid system, HyPoRes , with an algorithm that improves modular reductions for any prime modulo. The numbers are represented in a PMNS with coefficients in RNS. The modular reduction is faster than in conventional RNS for the primes standardized for ECC. In parallel, we are interested in a type of representation used to compute real solutions of fuzzy systems. We revisit the global approach of resolution using classical algebraic techniques and strengthen it. These results include a real system called the real transform that simplifies computations, and the management of the signs of the solutions
Agafonov, Evgeny. « Fuzzy and multi-resolution data processing for advanced traffic and travel information ». Thesis, Nottingham Trent University, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.271790.
Texte intégralCook, Brandon M. « An Intelligent System for Small Unmanned Aerial Vehicle Traffic Management ». University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1617106257481515.
Texte intégralSathyan, Anoop. « Intelligent Machine Learning Approaches for Aerospace Applications ». University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1491558309625214.
Texte intégralMuševič, Sašo. « Non-stationary sinusoidal analysis ». Doctoral thesis, Universitat Pompeu Fabra, 2013. http://hdl.handle.net/10803/123809.
Texte intégralMany types of everyday signals fall into the non-stationary sinusoids category. A large family of such signals represent audio, including acoustic/electronic, pitched/transient instrument sounds, human speech/singing voice, and a mixture of all: music. Analysis of such signals has been in the focus of the research community for decades. The main reason for such intense focus is the wide applicability of the research achievements to medical, financial and optical applications, as well as radar/sonar signal processing and system analysis. Accurate estimation of sinusoidal parameters is one of the most common digital signal processing tasks and thus represents an indispensable building block of a wide variety of applications. Classic time-frequency transformations are appropriate only for signals with slowly varying amplitude and frequency content - an assumption often violated in practice. In such cases, reduced readability and the presence of artefacts represent a significant problem. Time and frequency resolu
He, Guan-Sian, et 何冠賢. « Stability Analysis of Polynomial Fuzzy Systems ». Thesis, 2012. http://ndltd.ncl.edu.tw/handle/76065397030027969171.
Texte intégral國立中正大學
光機電整合工程研究所
100
This study presents a polynomial fuzzy model and a path controller design for a nonlinear four-wheeled omnidirectional mobile robot (ODMR) using polynomial fuzzy systems. A polynomial controller was designed according to the parallel distributed compensation (PDC) from the given polynomial fuzzy model of the ODMR. This proposed controller is capable of driving the closed-loop system states of the ODMR to follow reference trajectory commands. We used stability conditions that were represented by the sum of squares (SOS) to guarantee global stability. In addition, we derived the limitation conditions represented in term of SOS for control input and output using a polynomial Lyapunov function. The stable polynomial controller satisfied the constraint on the control input and output. These proposed SOS-based constraint conditions are more general and relaxed than are current linear matrix inequality (LMI)-based constraint conditions. This study focuses on developing methods for stability analysis and stabilization based on the SOS approach and that depend on the size of the time-delay. A polynomial Lyapunov function was applied to derive the stability and stabilization time-delay conditions of the nonlinear time-delay systems, and contained quadratic Lyapunov functions as a special case. Finally, computer simulations showed that the SOS-based approaches were more effective than were the LMI-based approaches.
WU, LING-YOU, et 吳凌侑. « Robust Switching Controller Design of Polynomial Fuzzy Systems ». Thesis, 2018. http://ndltd.ncl.edu.tw/handle/f63ca9.
Texte intégral國立中正大學
光機電整合工程研究所
106
In this thesis, a switching polynomial Lyapunov function is proposed to be applied to design robust switching controllers for Type-1 (T1) and interval Type-2 (IT2) polynomial fuzzy systems, respectively. The switching polynomial Lyapunov function partitions the membership function into operation intervals such that the feedback gain for each subinterval can be found and relaxed stability conditions can be acquired. In addition, the robust control performance of the system can be improved by deriving the relaxed stability conditions for the system with external disturbances and model uncertainties. Therefore, based on the switching polynomial Lyapunov function, seven relaxed stability conditions in terms of sum of squares (SOS) are proposed, which are the stability conditions of the switching T1 polynomial fuzzy systems with external disturbances, the stability conditions of the switching T1 polynomial fuzzy systems with model uncertainties, the robust stability conditions of the switching T1 polynomial fuzzy systems with external disturbances and model uncertainties, the stability conditions of the switching IT2 polynomial fuzzy systems, the stability conditions of the switching IT2 polynomial fuzzy systems with external disturbances, the stability conditions of the switching IT2 polynomial fuzzy systems with model uncertainties, and the robust stability conditions of the switching IT2 polynomial fuzzy systems with external disturbances and model uncertainties, respectively. Then computer simulations are carried out through two polynomial fuzzy model Examples to verify the effectiveness of the proposed robust controller against external disturbances and model uncertainties. Finally, the Theorems proposed in this thesis are realized by the tracking control experiments of the wheeled mobile robot (WMR).
HUANG, RUEY-SHENG, et 黃瑞盛. « Switching Polynomial Fuzzy Networked Control Systems of Mobile Robots ». Thesis, 2019. http://ndltd.ncl.edu.tw/handle/kx97h3.
Texte intégral國立中正大學
光機電整合工程研究所
107
This paper discusses the design of a switching polynomial fuzzy network control system, which are applied to the type-1 (T1) and interval type-2 (IT2) polynomial fuzzy networked controller by using the switching polynomial Lyapunov-Krasovskii function. The switching polynomial Lyapunov-Krasovskii function is composed of multiple local polynomial Lyapunov functions, which can expand the feasible region through relaxing stability conditions, so that the performance of the controller is better. Therefore, based on the switching polynomial Lyapunov-Krasovskii function, eight relaxed stability conditions in terms of sum of squares (SOS) are proposed, which are the stability conditions of switching T1 polynomial fuzzy networked control systems, the stability conditions of switching T1 polynomial fuzzy networked control systems with external disturbances, the stability conditions of switching T1 polynomial fuzzy networked control systems with model uncertainties, the robust stability conditions of switching T1 polynomial fuzzy networked control systems with external disturbances and model uncertainties, the stability conditions of switching IT2 polynomial fuzzy networked control systems, the stability conditions of switching IT2 polynomial fuzzy networked control systems with external disturbances, the stability conditions of switching IT2 polynomial fuzzy networked control systems with model uncertainties, the robust stability conditions of switching IT2 polynomial fuzzy networked control systems with external disturbances and model uncertainties. These stability conditions also consider the time delay and packet dropout caused by network control. Then a simulation is performed through a single-rigid robot and polynomial fuzzy models to verify the validity of the proposed theorem applied to the controller against external disturbances and model uncertainties. Finally, the wheeled mobile robot controller design is used for tracking control experiments to achieve the superiority of the theorem proposed in this paper.
Wang, Shun-Min, et 王舜民. « Output-Feedback Control of Networked Nonlinear Systems : Polynomial Fuzzy Approach ». Thesis, 2013. http://ndltd.ncl.edu.tw/handle/50396743892611438077.
Texte intégral國立中正大學
光機電整合工程研究所
101
This study makes use of the polynomial fuzzy approach for output-feedback control in networked control systems (NCSs) that are subject to external disturbances and model uncertainties, taking into account, the issues of network-induced delay and packet dropout in NCSs. A novel output feedback polynomial fuzzy controller design is proposed for nonlinear NCSs which are with external disturbances and model uncertainties. The authors utilized Lyapunov-Krasovskii functionals and the criterion to derive a theorem for robust stability conditions based on sum of squares (SOS), which can be numerically solved using the Matlab toolbox SOSTOOLS. The results of the simulations are provided to illustrate effectiveness of the static output feedback polynomial fuzzy controller design.
Livres sur le sujet "Resolution of fuzzy polynomial systems"
Lam, Hak-Keung. Polynomial Fuzzy Model-Based Control Systems. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-34094-4.
Texte intégralLam, Hak-Keung. Polynomial Fuzzy Model-Based Control Systems : Stability Analysis and Control Synthesis Using Membership Function Dependent Techniques. Springer, 2016.
Trouver le texte intégralLam, Hak-Keung. Polynomial Fuzzy Model-Based Control Systems : Stability Analysis and Control Synthesis Using Membership Function Dependent Techniques. Springer London, Limited, 2016.
Trouver le texte intégralLam, Hak-Keung. Polynomial Fuzzy Model-Based Control Systems : Stability Analysis and Control Synthesis Using Membership Function Dependent Techniques. Springer, 2018.
Trouver le texte intégralChapitres de livres sur le sujet "Resolution of fuzzy polynomial systems"
Massanet, Sebastia, Juan Vicente Riera et Daniel Ruiz-Aguilera. « On Fuzzy Polynomial Implications ». Dans Information Processing and Management of Uncertainty in Knowledge-Based Systems, 138–47. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08795-5_15.
Texte intégralPitarch, José Luis, Antonio Sala et Carlos Vicente Ariño. « Polynomial Fuzzy Systems : Stability and Control ». Dans Atlantis Computational Intelligence Systems, 95–115. Paris : Atlantis Press, 2014. http://dx.doi.org/10.2991/978-94-6239-082-9_5.
Texte intégralLam, Hak-Keung. « Stability Analysis of Polynomial Fuzzy Model-Based Control Systems Using Fuzzy Polynomial Lyapunov Function ». Dans Polynomial Fuzzy Model-Based Control Systems, 259–94. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-34094-4_10.
Texte intégralLam, Hak-Keung. « Stability Analysis of Polynomial Fuzzy Model-Based Control Systems Using Switching Polynomial Lyapunov Function ». Dans Polynomial Fuzzy Model-Based Control Systems, 223–58. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-34094-4_9.
Texte intégralLam, Hak-Keung. « Introduction ». Dans Polynomial Fuzzy Model-Based Control Systems, 3–38. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-34094-4_1.
Texte intégralLam, Hak-Keung. « Preliminaries ». Dans Polynomial Fuzzy Model-Based Control Systems, 39–58. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-34094-4_2.
Texte intégralLam, Hak-Keung. « Stability Analysis of Polynomial Fuzzy Model-Based Control Systems with Mismatched Premise Membership Functions Through Symbolic Variables ». Dans Polynomial Fuzzy Model-Based Control Systems, 61–83. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-34094-4_3.
Texte intégralLam, Hak-Keung. « Stability Analysis of Polynomial Fuzzy Model-Based Control Systems with Mismatched Premise Membership Functions Through Taylor Series Membership Functions ». Dans Polynomial Fuzzy Model-Based Control Systems, 85–102. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-34094-4_4.
Texte intégralLam, Hak-Keung. « Stability Analysis of General Polynomial Fuzzy Model-Based Control Systems ». Dans Polynomial Fuzzy Model-Based Control Systems, 103–34. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-34094-4_5.
Texte intégralLam, Hak-Keung. « Output Regulation of Polynomial Fuzzy Model-Based Control Systems ». Dans Polynomial Fuzzy Model-Based Control Systems, 137–73. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-34094-4_6.
Texte intégralActes de conférences sur le sujet "Resolution of fuzzy polynomial systems"
LAZARD, D. « RESOLUTION OF POLYNOMIAL SYSTEMS ». Dans Proceedings of the Fourth Asian Symposium (ASCM 2000). WORLD SCIENTIFIC, 2000. http://dx.doi.org/10.1142/9789812791962_0001.
Texte intégralLi, Guiling, et Chen Peng. « Event-Triggered Polynomial Fuzzy Controller for Networked Polynomial Fuzzy Systems ». Dans 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS). IEEE, 2018. http://dx.doi.org/10.1109/ccis.2018.8691220.
Texte intégralAmmar, Imen Iben, Hamdi Gassara, Ahmed El Hajjaji et Mohamed Chaabane. « Robust Polynomial Observers For Positive Polynomial Fuzzy Systems ». Dans 2021 18th International Multi-Conference on Systems, Signals & Devices (SSD). IEEE, 2021. http://dx.doi.org/10.1109/ssd52085.2021.9429314.
Texte intégralMoreno Saenz, Jairo, Motoyasu Tanaka et Kazuo Tanaka. « Control Synthesis for Polynomial Fuzzy Systems Using Line-Integral Polynomial Fuzzy Lyapunov Function ». Dans 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2018. http://dx.doi.org/10.1109/smc.2018.00498.
Texte intégralChen, Ziran, Baoyong Zhang et Qi Zhou. « Filtering for polynomial fuzzy systems using polynomial approximated membership functions ». Dans 2015 IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). IEEE, 2015. http://dx.doi.org/10.1109/cyber.2015.7288199.
Texte intégralChen, Ying-Jen, Motoyasu Tanaka, Kazuo Tanaka et Hua O. Wang. « Piecewise polynomial lyapunov functions based stability analysis for polynomial fuzzy systems ». Dans 2013 IEEE International Conference on Control System, Computing and Engineering (ICCSCE). IEEE, 2013. http://dx.doi.org/10.1109/iccsce.2013.6719928.
Texte intégralChen, Ying-Jen, Motoyasu Tanaka, Kazuo Tanaka et Hua O. Wang. « Stability region analysis for polynomial fuzzy systems by polynomial Lyapunov functions ». Dans 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2014. http://dx.doi.org/10.1109/fuzz-ieee.2014.6891529.
Texte intégralKim, Han Sol, Jin Bae Park et Young Hoon Joo. « Further relaxed stability conditions for continuous-time polynomial fuzzy system based on polynomial fuzzy Lyapunov function ». Dans 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2012. http://dx.doi.org/10.1109/fuzz-ieee.2012.6251316.
Texte intégralPang, Bo, Xianwen Gao et Xiang Sheng. « Controllability of uncertain polynomial fuzzy singular systems ». Dans 2020 39th Chinese Control Conference (CCC). IEEE, 2020. http://dx.doi.org/10.23919/ccc50068.2020.9188440.
Texte intégralYing-Jen Chen, Motoyasu Tanaka, Kazuo Tanaka et Hua O. Wang. « Nonconvex stabilization criterion for polynomial fuzzy systems ». Dans 2013 IEEE 52nd Annual Conference on Decision and Control (CDC). IEEE, 2013. http://dx.doi.org/10.1109/cdc.2013.6761066.
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