Relevant bibliographies by topics / Resolution of fuzzy polynomial systems

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Resolution of fuzzy polynomial systems'

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Published: 25 May 2024

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Journal articles on the topic "Resolution of fuzzy polynomial systems"

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Adil, Bouhouch, Er-Rafyg Aicha, and Ez-Zahout Abderrahmane. "Neural network to solve fuzzy constraint satisfaction problems." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (March 1, 2024): 228. http://dx.doi.org/10.11591/ijai.v13.i1.pp228-235.

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<p>It has been proven that solving the constraint satisfaction problem (CSP) is an No Polynomial hard combinatorial optimization problem. This holds true even in cases where the constraints are fuzzy, known as fuzzy constraint satisfaction problems (FCSP). Therefore, the continuous Hopfield neural network model can be utilized to resolve it. The original algorithm was developed by Talaavan in 2005. Many practical problems can be represented as a FCSP. In this paper, we expand on a neural network technique that was initially developed for solving CSP and adapt it to tackle problems that involve at least one fuzzy constraint. To validate the enhanced effectiveness and rapid convergence of our proposed approach, a series of numerical experiments are carried out. The results of these experiments demonstrate the superior performance of the new method. Additionally, the experiments confirm its fast convergence. Specifically, our study focuses on binary instances with ordinary constraints to test the proposed resolution model. The results confirm that both the proposed approaches and the original continuous Hopfield neural network approach exhibit similar performance and robustness in solving ordinary constraint satisfaction problems.</p>
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German, Oleg, and Sara Nasrh. "New Method for Optimal Feature Set Reduction." Informatics and Automation 19, no. 6 (December 11, 2020): 1198–221. http://dx.doi.org/10.15622/ia.2020.19.6.3.

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A problem of searching a minimum-size feature set to use in distribution of multidimensional objects in classes, for instance with the help of classifying trees, is considered. It has an important value in developing high speed and accuracy classifying systems. A short comparative review of existing approaches is given. Formally, the problem is formulated as finding a minimum-size (minimum weighted sum) covering set of discriminating 0,1-matrix, which is used to represent capabilities of the features to distinguish between each pair of objects belonging to different classes. There is given a way to build a discriminating 0,1-matrix. On the basis of the common solving principle, called the group resolution principle, the following problems are formulated and solved: finding an exact minimum-size feature set; finding a feature set with minimum total weight among all the minimum-size feature sets (the feature weights may be defined by the known methods, e.g. the RELIEF method and its modifications); finding an optimal feature set with respect to fuzzy data and discriminating matrix elements belonging to diapason [0,1]; finding statistically optimal solution especially in the case of big data. Statistically optimal algorithm makes it possible to restrict computational time by a polynomial of the problem sizes and density of units in discriminating matrix and provides a probability of finding an exact solution close to 1. Thus, the paper suggests a common approach to finding a minimum-size feature set with peculiarities in problem formulation, which differs it from the known approaches. The paper contains a lot of illustrations for clarification aims. Some theoretical statements given in the paper are based on the previously published works. In the concluding part, the results of the experiments are presented, as well as the information on dimensionality reduction for the coverage problem for big datasets. Some promising directions of the outlined approach are noted, including working with incomplete and categorical data, integrating the control model into the data classification system.
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Chen, Ying-Jen, Hua O. Wang, Motoyasu Tanaka, Kazuo Tanaka, and Hiroshi Ohtake. "Discrete polynomial fuzzy systems control." IET Control Theory & Applications 8, no. 4 (March 6, 2014): 288–96. http://dx.doi.org/10.1049/iet-cta.2013.0645.

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Qiu, Yu, Hong Yang, Yan-Qing Zhang, and Yichuan Zhao. "Polynomial regression interval-valued fuzzy systems." Soft Computing 12, no. 2 (May 23, 2007): 137–45. http://dx.doi.org/10.1007/s00500-007-0189-4.

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Aubry, Philippe, Jérémy Marrez, and Annick Valibouze. "Computing real solutions of fuzzy polynomial systems." Fuzzy Sets and Systems 399 (November 2020): 55–76. http://dx.doi.org/10.1016/j.fss.2020.01.004.

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OH, S., W. PEDRYCZ, and S. ROH. "Genetically optimized fuzzy polynomial neural networks with fuzzy set-based polynomial neurons." Information Sciences 176, no. 23 (December 4, 2006): 3490–519. http://dx.doi.org/10.1016/j.ins.2005.11.009.

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Ku, Cheung-Chieh, Chein-Chung Sun, Shao-Hao Jian, and Wen-Jer Chang. "Passive Fuzzy Controller Design for the Parameter-Dependent Polynomial Fuzzy Model." Mathematics 11, no. 11 (May 28, 2023): 2482. http://dx.doi.org/10.3390/math11112482.

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This paper discusses a passive control issue for Nonlinear Time-Varying (NTV) systems subject to stability and attenuation performance. Based on the modeling approaches of Takagi-Sugeno (T-S) fuzzy model and Linear Parameter-Varying (LPV) model, a Parameter-Dependent Polynomial Fuzzy (PDPF) model is constructed to represent NTV systems. According to the Parallel Distributed Compensation (PDC) concept, a parameter-dependent polynomial fuzzy controller is built to achieve robust stability and passivity of the PDPF model. Furthermore, the passive theory is applied to achieve performance, constraining the disturbance effect on the PDPF systems. To develop the stability criteria, by introducing a parameter-dependent polynomial Lyapunov function, one can derive some stability conditions, which belong to the term of Sum-Of-Squares (SOS) form. Based on the Lyapunov function, two stability criteria are proposed to design the corresponding PDPF controller, such that the NTV system is robustly stable and passive. Finally, two examples are applied to demonstrate the effectiveness of the proposed stability criterion.
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Kharrati, Hamed, Sohrab Khanmohammadi, Witold Pedrycz, and 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.

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This study presents an improved model and controller for nonlinear plants using polynomial fuzzy model-based (FMB) systems. To minimize mismatch between the polynomial fuzzy model and nonlinear plant, the suitable parameters of membership functions are determined in a systematic way. Defining an appropriate fitness function and utilizing Taylor series expansion, a genetic algorithm (GA) is used to form the shape of membership functions in polynomial forms, which are afterwards used in fuzzy modeling. To validate the model, a controller based on proposed polynomial fuzzy systems is designed and then applied to both original nonlinear plant and fuzzy model for comparison. Additionally, stability analysis for the proposed polynomial FMB control system is investigated employing Lyapunov theory and a sum of squares (SOS) approach. Moreover, the form of the membership functions is considered in stability analysis. The SOS-based stability conditions are attained using SOSTOOLS. Simulation results are also given to demonstrate the effectiveness of the proposed method.
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Shen, Yu-Hsuan, Ying-Jen Chen, Fan-Nong Yu, Wen-June Wang, and Kazuo Tanaka. "Descriptor Representation-Based Guaranteed Cost Control Design Methodology for Polynomial Fuzzy Systems." Processes 10, no. 9 (September 7, 2022): 1799. http://dx.doi.org/10.3390/pr10091799.

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This paper presents a descriptor representation-based guaranteed cost design methodology for polynomial fuzzy systems. This methodology applies the descriptor representation for presenting the closed-loop system of the polynomial fuzzy model with a parallel distributed compensation (PDC) based fuzzy controller. By the utility of descriptor representation, the guaranteed cost control (GCC) design analysis can utilize polynomial fuzzy slack matrices for obtaining less conservative results. The proposed GCC design is presented as the sum-of-squares (SOS) conditions. The application of polynomial fuzzy slack matrices leads to the double fuzzy summation issue in the control design. Accordingly, the copositive relaxation works out the problem well and is adopted in the control design analysis. The GCC design minimizes the upper limit of a predesignated cost function. According to the performance function, two simulation examples are provided to demonstrate the validity of the proposed GCC design. In these two examples, the proposed design obtains superior results.
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Nasiri, Alireza, Sing Kiong Nguang, Akshya Swain, and Dhafer Almakhles. "Stabilisation of discrete-time polynomial fuzzy systems via a polynomial lyapunov approach." International Journal of Systems Science 49, no. 3 (December 21, 2017): 557–66. http://dx.doi.org/10.1080/00207721.2017.1407006.

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Dissertations / Theses on the topic "Resolution of fuzzy polynomial systems"

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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.

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The main research objective in this thesis is to investigate the stability and performance of the interval type-2 (IT2) polynomial-fuzzy-model-based (PFMB) control system. PFMB control scheme has been developed recently around 2009 and demonstrates more potential than the traditional Takagi-Sugeno fuzzy-model-based (T-S FMB) control approach to represent the nonlinearities in the plant. Meanwhile, the IT2 fuzzy logic has also been proposed to incorporate uncertainties of the nonlinear systems into the membership functions directly. Through the IT2 PFMB control design approach, both the nonlinearity and the uncertainty in the system can be handled well. The control performance and the relaxation of stability conditions of IT2 PFMB control systems are studied and investigated in the thesis. The main contribution of the thesis is summarized in three tasks and presented as following: In the first task in Chapter 3, the stability conditions of the PFMB systems equipped with mismatched IT2 membership functions are investigated. Unlike the membership-function-independent (MFI) methods, the information and properties of IT2 membership functions are considered in the stability analysis and contained in the stability conditions in terms of sum-of-squares (SOS) based on the Lyapunov stability theory. Three methods, demonstrating their own merits, are proposed to conduct the stability analysis for the IT2 PFMB control systems and all of the methods can achieve feasible control results. All the three approaches are well explained, which offers the reader systematic ways to include the information of the membership functions into the analysis. In addition, all the approaches are compared and the pros and cons are presented to help the reader choose the most appropriate approach in the applications. In the second task presented in Chapter 4, the membership-functions-dependent (MFD) methods have been proceeded to the tracking control problems and the output feedback tracking issues of IT2 PFMB fuzzy control systems are investigated. The output-feedback IT2 polynomial fuzzy controller connected with the nonlinear plant in a closed loop drives the system states of the nonlinear plant to track those of the stable reference model. The system stability is investigated based on the Lyapunov stability theory under the SOS-based analysis approach and the SOSbased stability conditions are derived subject to a prescribed H1 performance. Like in the first work, the information of membership functions is also included in the analysis to facilitate the analysis and help improve the tracking performance in terms of H1 performance. Considering the implementation of the mentioned control schemes on digital computers, the sampled-data control systems are investigated as the last work in the thesis, which is presented in Chapter 5. In this task, the IT2 PFMB tracking control system is extended to the sampled-data based one. Through using the sampled output of both the control system and the reference system, an IT2 polynomial sampled-data based output feedback fuzzy controller can be designed to ful ll the tracking control task, the stability conditions can be obtained in terms of SOS and the tracking error is attenuated by the H1 performance index. As did in the previous two works, the information of the IT2 membership functions is used to relax the stability conditions and improve the tracking performance. The approaches proposed in the thesis to relax the stability conditions as well as to improve the tracking performance of the IT2 PFMB control systems are proved through the Lyapunov based stability theory. Meanwhile, simulation examples are provided to demonstrate and verify the theoretical analysis.
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Marrez, 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.

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Les calculs modulaires entrant en jeu dans les applications en cryptographie asymétrique utilisent le plus souvent un modulo premier standardisé, dont le choix n’est pas toujours libre en pratique. L’amélioration des opérations modulaires est centrale pour l’efficacité et la sécurité de ces primitives. Cette thèse propose de fournir une arithmétique modulaire efficace pour le plus grand nombre de premiers possible, tout en la prémunissant contre certains types d’attaques. Pour ce faire, nous nous intéressons au système PMNS utilisé pour l’arithmétique modulaire, et proposons des méthodes afin d’obtenir de nombreux PMNS pour un premier donné, avec une arithmétique efficace sur les représentations. Nous considérons également la randomisation des calculs modulaires via des algorithmes de type Montgomery et Babaï en exploitant la redondance intrinsèque aux PMNS. Les changements induits de représentation des données au cours du calcul empêchent un attaquant d’effectuer des hypothèses utiles sur ces représentations. Nous présentons ensuite un système hybride, HyPoRes, avec un algorithme améliorant les réductions modulaires pour tout modulo premier. Les nombres sont représentés dans un PMNS avec des coefficients en RNS. La réduction modulaire est plus rapide qu’en RNS classique pour les premiers standardisés pour ECC. En parallèle, nous étudions un type de représentation utilisé pour la résolution réelle de systèmes flous. Nous revisitons l’approche globale de résolution faisant appel à des techniques algébriques classiques et la renforçons. Ces résultats incluent un système réel appelé la transformation réelle qui simplifie les calculs, et la gestion des signes des solutions
Modular 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
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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.

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Cook, 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.

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Sathyan, Anoop. "Intelligent Machine Learning Approaches for Aerospace Applications." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1491558309625214.

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Muševič, Sašo. "Non-stationary sinusoidal analysis." Doctoral thesis, Universitat Pompeu Fabra, 2013. http://hdl.handle.net/10803/123809.

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Muchos tipos de señales que encontramos a diario pertenecen a la categoría de sinusoides no estacionarias. Una gran parte de esas señales son sonidos que presentan una gran variedad de características: acústicos/electrónicos, sonidos instrumentales harmónicos/impulsivos, habla/canto, y la mezcla de todos ellos que podemos encontrar en la música. Durante décadas la comunidad científica ha estudiado y analizado ese tipo de señales. El motivo principal es la gran utilidad de los avances científicos en una gran variedad de áreas, desde aplicaciones médicas, financiera y ópticas, a procesado de radares o sonar, y también a análisis de sistemas. La estimación precisa de los parámetros de sinusoides no estacionarias es una de las tareas más comunes en procesado digital de señales, y por lo tanto un elemento fundamental e indispensable para una gran variedad de aplicaciones. Las transformaciones de tiempo y frecuencia clásicas son solamente apropiadas para señales con variación lenta de amplitud y frecuencia. Esta suposición no suele cumplirse en la práctica, lo que conlleva una degradación de calidad y la aparición de artefactos. Además, la resolución temporal y frecuencial no se puede incrementar arbitrariamente debido al conocido principio de incertidumbre de Heisenberg. \\ El principal objetivo de esta tesis es revisar y mejorar los métodos existentes para el análisis de sinusoides no estacionarias, y también proponer nuevas estrategias y aproximaciones. Esta disertación contribuye sustancialmente a los análisis sinusoidales existentes: a) realiza una evaluación crítica del estado del arte y describe con gran detalle los métodos de análisis existentes, b) aporta mejoras sustanciales a algunos de los métodos existentes más prometedores, c) propone varias aproximaciones nuevas para el análisis de los modelos sinusoidales existentes i d) propone un modelo sinusoidal muy general y flexible con un algoritmo de análisis directo y rápido.
Many 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
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He, Guan-Sian, and 何冠賢. "Stability Analysis of Polynomial Fuzzy Systems." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/76065397030027969171.

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碩士
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.
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WU, LING-YOU, and 吳凌侑. "Robust Switching Controller Design of Polynomial Fuzzy Systems." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/f63ca9.

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碩士
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).
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HUANG, RUEY-SHENG, and 黃瑞盛. "Switching Polynomial Fuzzy Networked Control Systems of Mobile Robots." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/kx97h3.

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碩士
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.
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Wang, Shun-Min, and 王舜民. "Output-Feedback Control of Networked Nonlinear Systems: Polynomial Fuzzy Approach." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/50396743892611438077.

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碩士
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.
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Books on the topic "Resolution of fuzzy polynomial systems"

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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.

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Lam, Hak-Keung. Polynomial Fuzzy Model-Based Control Systems: Stability Analysis and Control Synthesis Using Membership Function Dependent Techniques. Springer, 2016.

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Lam, Hak-Keung. Polynomial Fuzzy Model-Based Control Systems: Stability Analysis and Control Synthesis Using Membership Function Dependent Techniques. Springer London, Limited, 2016.

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Lam, Hak-Keung. Polynomial Fuzzy Model-Based Control Systems: Stability Analysis and Control Synthesis Using Membership Function Dependent Techniques. Springer, 2018.

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Book chapters on the topic "Resolution of fuzzy polynomial systems"

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Massanet, Sebastia, Juan Vicente Riera, and Daniel Ruiz-Aguilera. "On Fuzzy Polynomial Implications." In 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.

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Pitarch, José Luis, Antonio Sala, and Carlos Vicente Ariño. "Polynomial Fuzzy Systems: Stability and Control." In Atlantis Computational Intelligence Systems, 95–115. Paris: Atlantis Press, 2014. http://dx.doi.org/10.2991/978-94-6239-082-9_5.

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Lam, Hak-Keung. "Stability Analysis of Polynomial Fuzzy Model-Based Control Systems Using Fuzzy Polynomial Lyapunov Function." In 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.

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Lam, Hak-Keung. "Stability Analysis of Polynomial Fuzzy Model-Based Control Systems Using Switching Polynomial Lyapunov Function." In 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.

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Lam, Hak-Keung. "Introduction." In 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.

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Lam, Hak-Keung. "Preliminaries." In 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.

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Lam, Hak-Keung. "Stability Analysis of Polynomial Fuzzy Model-Based Control Systems with Mismatched Premise Membership Functions Through Symbolic Variables." In 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.

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Lam, Hak-Keung. "Stability Analysis of Polynomial Fuzzy Model-Based Control Systems with Mismatched Premise Membership Functions Through Taylor Series Membership Functions." In 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.

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Lam, Hak-Keung. "Stability Analysis of General Polynomial Fuzzy Model-Based Control Systems." In 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.

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Lam, Hak-Keung. "Output Regulation of Polynomial Fuzzy Model-Based Control Systems." In 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.

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Conference papers on the topic "Resolution of fuzzy polynomial systems"

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LAZARD, D. "RESOLUTION OF POLYNOMIAL SYSTEMS." In Proceedings of the Fourth Asian Symposium (ASCM 2000). WORLD SCIENTIFIC, 2000. http://dx.doi.org/10.1142/9789812791962_0001.

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Li, Guiling, and Chen Peng. "Event-Triggered Polynomial Fuzzy Controller for Networked Polynomial Fuzzy Systems." In 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS). IEEE, 2018. http://dx.doi.org/10.1109/ccis.2018.8691220.

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Ammar, Imen Iben, Hamdi Gassara, Ahmed El Hajjaji, and Mohamed Chaabane. "Robust Polynomial Observers For Positive Polynomial Fuzzy Systems." In 2021 18th International Multi-Conference on Systems, Signals & Devices (SSD). IEEE, 2021. http://dx.doi.org/10.1109/ssd52085.2021.9429314.

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Moreno Saenz, Jairo, Motoyasu Tanaka, and Kazuo Tanaka. "Control Synthesis for Polynomial Fuzzy Systems Using Line-Integral Polynomial Fuzzy Lyapunov Function." In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2018. http://dx.doi.org/10.1109/smc.2018.00498.

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Chen, Ziran, Baoyong Zhang, and Qi Zhou. "Filtering for polynomial fuzzy systems using polynomial approximated membership functions." In 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.

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Chen, Ying-Jen, Motoyasu Tanaka, Kazuo Tanaka, and Hua O. Wang. "Piecewise polynomial lyapunov functions based stability analysis for polynomial fuzzy systems." In 2013 IEEE International Conference on Control System, Computing and Engineering (ICCSCE). IEEE, 2013. http://dx.doi.org/10.1109/iccsce.2013.6719928.

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Chen, Ying-Jen, Motoyasu Tanaka, Kazuo Tanaka, and Hua O. Wang. "Stability region analysis for polynomial fuzzy systems by polynomial Lyapunov functions." In 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2014. http://dx.doi.org/10.1109/fuzz-ieee.2014.6891529.

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Kim, Han Sol, Jin Bae Park, and Young Hoon Joo. "Further relaxed stability conditions for continuous-time polynomial fuzzy system based on polynomial fuzzy Lyapunov function." In 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2012. http://dx.doi.org/10.1109/fuzz-ieee.2012.6251316.

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Pang, Bo, Xianwen Gao, and Xiang Sheng. "Controllability of uncertain polynomial fuzzy singular systems." In 2020 39th Chinese Control Conference (CCC). IEEE, 2020. http://dx.doi.org/10.23919/ccc50068.2020.9188440.

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Ying-Jen Chen, Motoyasu Tanaka, Kazuo Tanaka, and Hua O. Wang. "Nonconvex stabilization criterion for polynomial fuzzy systems." In 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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