Log in

Relevant bibliographies by topics / Resolution of fuzzy polynomial systems

Contents

Journal articles

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

Author: Grafiati

Published: 25 May 2024

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Resolution of fuzzy polynomial systems.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

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

1

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 (2024): 228. http://dx.doi.org/10.11591/ijai.v13.i1.pp228-235.

Full text

Abstract:

<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>

APA, Harvard, Vancouver, ISO, and other styles

2

German, Oleg, and Sara Nasrh. "New Method for Optimal Feature Set Reduction." Informatics and Automation 19, no. 6 (2020): 1198–221. http://dx.doi.org/10.15622/ia.2020.19.6.3.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

3

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 (2014): 288–96. http://dx.doi.org/10.1049/iet-cta.2013.0645.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

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 (2023): 2482. http://dx.doi.org/10.3390/math11112482.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

9

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 (2022): 1799. http://dx.doi.org/10.3390/pr10091799.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

10

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 (2017): 557–66. http://dx.doi.org/10.1080/00207721.2017.1407006.

Full text

APA, Harvard, Vancouver, ISO, and other styles

More sources

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!