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Journal articles on the topic 'Fuzzy model identification'

Author: Grafiati

Published: 3 June 2025

Last updated: 19 July 2025

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1

Hwang, H. S., and K. B. Woo. "Linguistic fuzzy model identification." IEE Proceedings - Control Theory and Applications 142, no. 6 (1995): 537–44. http://dx.doi.org/10.1049/ip-cta:19952254.

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2

Burakov, Mikhail Vladimirovich, and Maksim Sergeevich Brunov. "Structural Identification of Fuzzy Model." SPIIRAS Proceedings 3, no. 34 (2014): 232. http://dx.doi.org/10.15622/sp.34.12.

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3

Park, Jong-Il, Jae-Heung Oh, and Young-Hoon Joo. "Fuzzy Model Identification Using VmGA." International Journal of Fuzzy Logic and Intelligent Systems 2, no. 1 (2002): 53–58. http://dx.doi.org/10.5391/ijfis.2002.2.1.053.

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4

Sugeno, M., and G. T. Kang. "Structure identification of fuzzy model." Fuzzy Sets and Systems 28, no. 1 (1988): 15–33. http://dx.doi.org/10.1016/0165-0114(88)90113-3.

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5

Joo, Y. H., K. B. Kim, K. B. Woo, and H. S. Hwang. "Linguistic model identification for fuzzy system." Electronics Letters 31, no. 4 (1995): 330–31. http://dx.doi.org/10.1049/el:19950163.

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6

Shi, Jianzhong. "Identification of Circulating Fluidized Bed Boiler Bed Temperature Based on Hyper-Plane-Shaped Fuzzy C-Regression Model." International Journal of Computational Intelligence and Applications 19, no. 04 (2020): 2050029. http://dx.doi.org/10.1142/s1469026820500297.

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Bed temperature in dense-phase zone is the key parameter of circulating fluidized bed (CFB) boiler for stable combustion and economic operation. It is difficult to establish an accurate bed temperature model as the complexity of circulating fluidized bed combustion system. T-S fuzzy model was widely applied in the system identification for it can approximate complex nonlinear system with high accuracy. Fuzzy c-regression model (FCRM) clustering based on hyper-plane-shaped distance has the advantages in describing T-S fuzzy model, and Gaussian function was adapted in antecedent membership function of T-S fuzzy model. However, Gaussian fuzzy membership function was more suitable for clustering algorithm using point to point distance, such as fuzzy c-means (FCM). In this paper, a hyper-plane-shaped FCRM clustering algorithm for T-S fuzzy model identification algorithm is proposed. The antecedent membership function of proposed identification algorithm is defined by a hyper-plane-shaped membership function and an improved fuzzy partition method is applied. To illustrate the efficiency of the proposed identification algorithm, the algorithm is applied in four nonlinear systems which shows higher identification accuracy and simplified identification process. At last, the algorithm is used in a circulating fluidized bed boiler bed temperature identification process, and gets better identification result.

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Ho, Anh Pham Huy, and Nam Thanh Nguyen. "Dynamic model identification of IPMC actuator using fuzzy NARX model optimized by MPSO." Science and Technology Development Journal 17, no. 1 (2014): 62–80. http://dx.doi.org/10.32508/stdj.v17i1.1295.

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In this paper, a novel inverse dynamic fuzzy NARX model is used for modeling and identifying the IPMC-based actuator’s inverse dynamic model. The contact force variation and highly nonlinear cross effect of the IPMC-based actuator are thoroughly modeled based on the inverse fuzzy NARX model-based identification process using experiment input-output training data. This paper proposes the novel use of a modified particle swarm optimization (MPSO) to generate the inverse fuzzy NARX (IFN) model for a highly nonlinear IPMC actuator system. The results show that the novel inverse dynamic fuzzy NARX model trained by MPSO algorithm yields outstanding performance and perfect accuracy.

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Nibiret, Getinet Asimare, and Abrham Tadesse Kassie. "Fuzzy Model Based Model Predictive Control for Biomass Boiler." International Journal of Engineering Research in Africa 71 (September 18, 2024): 93–108. http://dx.doi.org/10.4028/p-6uv4x4.

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In the realm of renewable energy, biomass plays a crucial role. A key component of power plants, the biomass boiler unit, is responsible for steam production. This unit operates as a nonlinear, highly coupled multivariable process. Traditional controllers used in the industry are ineffective for such systems. To address this, this paper presents a novel approach: a model predictive controller designed for biomass boiler plants. Fuzzy modelling, employed to approximate nonlinear functions to linear ones, is used for system identification. The methodology is implemented using MATLAB/Simulink and the Fuzzy modelling and identification (FMID) toolbox, utilizing input-output data from the Wenji-Shoa sugar factory for fuzzy model identification. The proposed controller demonstrates significant improvements, achieving settling times of 7.5, 13, and 7 seconds, with acceptable overshoots of 0.5%, 0.39%, and 0.46% for pressure, temperature, and level, respectively, for MISO systems. In contrast, the MPC shows improved performance in MIMO systems compared to MISO systems, with settling times of 5, 4, and 7 seconds, while the overshoot is reduced only for the pressure output, with 0.214%.

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Bertone, Ana Maria Amarillo, Jefferson Beethoven Martins, and Keiji Yamanaka. "Black-Box Fuzzy Identification of a Nonlinear Hydrogen Fuel Cell Model." TEMA (São Carlos) 18, no. 3 (2018): 405. http://dx.doi.org/10.5540/tema.2017.018.03.405.

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A fuzzy identification of the dynamical system model is developed upon a data generated by a software simulator of a hydrogen fuel cell. The data presents a black box model, just composed by inputs and outputs, carry no additional information, and showing a strong nonlinear behavior. The choice for a fuzzy identification is based on the data features, and the malleability of the mathematical fuzzy technique. This approach allows to accomplish the objectives of the research, among which, the validation of the method for it used in other industrial problems. The dynamic system identification process is performed using a fuzzy clustering through the Gustafson and Kessel algorithm, and a Takagi Sugeno fuzzy inference method. Validation tests are performed in terms of the 4-fold technique, confirming the lack of the data over-training. These results make the fuzzy approach looks as a promising tool for black-box identification of non linear dynamic systems.

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10

Kumar, Shakti, Parvinder Kaur, and Amarpartap Singh. "Fuzzy Model Identification: A Firefly Optimization Approach." International Journal of Computer Applications 58, no. 6 (2012): 1–8. http://dx.doi.org/10.5120/9283-3475.

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11

Chiu, Stephen L. "Fuzzy Model Identification Based on Cluster Estimation." Journal of Intelligent and Fuzzy Systems 2, no. 3 (1994): 267–78. http://dx.doi.org/10.3233/ifs-1994-2306.

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12

Ying-Chin Lee, Ehyi Hwang, and Yen-Ping Shih. "A combined approach to fuzzy model identification." IEEE Transactions on Systems, Man, and Cybernetics 24, no. 5 (1994): 736–44. http://dx.doi.org/10.1109/21.293487.

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13

Nagai, Elaine Yassue, and Lucia Valeria Ramos de Arruda. "SOFT SENSOR BASED ON FUZZY MODEL IDENTIFICATION." IFAC Proceedings Volumes 38, no. 1 (2005): 127–32. http://dx.doi.org/10.3182/20050703-6-cz-1902.01099.

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14

Abonyi, J., R. Babuska, H. B. Verbruggen, and F. Szeifert. "Incorporating prior knowledge in fuzzy model identification." International Journal of Systems Science 31, no. 5 (2000): 657–67. http://dx.doi.org/10.1080/002077200290966.

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15

Chen, Jian-Qin, Yu-Geng Xi, and Zhong-Jun Zhang. "A clustering algorithm for fuzzy model identification." Fuzzy Sets and Systems 98, no. 3 (1998): 319–29. http://dx.doi.org/10.1016/s0165-0114(96)00384-3.

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16

Skrjanc, I., S. Blazic, and O. Agamennoni. "Interval Fuzzy Model Identification Using$l_infty$-Norm." IEEE Transactions on Fuzzy Systems 13, no. 5 (2005): 561–68. http://dx.doi.org/10.1109/tfuzz.2005.856567.

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17

Hartmann, Benjamin, Oliver Banfer, Oliver Nelles, Anton Sodja, Luka Teslic, and Igor Skrjanc. "Supervised Hierarchical Clustering in Fuzzy Model Identification." IEEE Transactions on Fuzzy Systems 19, no. 6 (2011): 1163–76. http://dx.doi.org/10.1109/tfuzz.2011.2164256.

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18

Lim, Chern Hong, and Chee Seng Chan. "Fuzzy qualitative human model for viewpoint identification." Neural Computing and Applications 27, no. 4 (2015): 845–56. http://dx.doi.org/10.1007/s00521-015-1900-5.

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19

Yang, S. M., C. J. Chen, Y. Y. Chang, and Y. Z. Tung. "Development of a Self-Organized Neuro-Fuzzy Model for System Identification." Journal of Vibration and Acoustics 129, no. 4 (2006): 507–13. http://dx.doi.org/10.1115/1.2731417.

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It has been known that it is difficult to establish a fuzzy logic model with effective fuzzy rules and the associated membership functions. Neural network with its learning capability has been incorporated to make the fuzzy model more adaptive and effective. A self-organized neuro-fuzzy model by integrating the Mamdani fuzzy model and the backpropagation neural network is developed in this paper for system identification. The five-layer network adaptively adjusts the membership functions and dynamically optimizes the fuzzy rules. A benchmark test is applied to validate the model accuracy in nonlinear system identification. Experimental verifications on the dynamics of a composite smart structure and on an acoustics system also demonstrate that the neuro-fuzzy model is superior to the neural network and to an adaptive filter in system identification. The model can be established systematically and is shown to be effective in engineering applications.

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20

Zhang, Ke, Wenning Hao, Xiaohan Yu, and Tianhao Shao. "A Symmetrical Fuzzy Neural Network Regression Method Coordinating Structure and Parameter Identifications for Regression." Symmetry 15, no. 9 (2023): 1711. http://dx.doi.org/10.3390/sym15091711.

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Fuzzy neural networks have both the interpretability of fuzzy systems and the self-learning ability of neural networks, but they will face the challenge of “rule explosion” when dealing with high-dimensional data. Moreover, the structure and parameter identifications of models are generally performed in two stages, and this always attends to one thing and loses another in terms of interpretability and predictive performance. In this paper, a fuzzy neural network regression method (FNNR) that coordinates structure identification and parameter identification is proposed. To alleviate the problem of rule explosion, the structure identification and parameter identification are coordinated in the training process, and the numbers of fuzzy rules and fuzzy partitions are effectively limited, while the parameters of fuzzy rules are optimized. The symmetrical architecture of the FNNR is designed for automatic structure identification. An alternate training strategy is adopted by treating discrete and continuous parameters differently, and thus the convergence efficiency of the algorithm is improved. To enhance interpretability, regularized terms are designed from fuzzy rule level and fuzzy partition level to guide the model to learn fuzzy rules with simple structures and clear semantics. The experimental results show that the proposed method has both a compact structure and high precision.

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21

MENG, HSIEN-KANG, and WEI-LING CHIANG. "APPLICATION OF IDENTIFICATION OF FUZZY MODEL IN STRUCTURAL MECHANICS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 02, no. 03 (1994): 297–304. http://dx.doi.org/10.1142/s0218488594000249.

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In the conventional analysis of structural mechanics, the structural behaviour are determined in mathematical functions and based on idealized assumptions. But structural members do not behaviour as the description of the mathematical functions if the system is too complicated. Consequently, we don't have an appropriate methodology and an convenient algorithm to utilize. If we identify the structural system with a fuzzy model, [Formula: see text], where [Formula: see text] is the fuzzy relation, the procedure of the system identification will be simplified and the model can be quickly constructed 110 matter the style of nonlinear form and the orders. In this paper, four composition operators (α, β, φ and t) will be used to derive fuzzy relations from given data and then applied to two interesting problems of structural mechanics for illustrating the reliability ty and efficiency of the theory of fuzzy model.

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22

Gómez-Skarmeta, A. F., M. Delgado, and M. A. Vila. "About the use of fuzzy clustering techniques for fuzzy model identification." Fuzzy Sets and Systems 106, no. 2 (1999): 179–88. http://dx.doi.org/10.1016/s0165-0114(97)00276-5.

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23

Subiantoro, Aries, F. Yusivar, B. Budiardjo, and M. I. Al-Hamid. "Identification and Control Design of Fuzzy Takagi-Sugeno Model for Pressure Process Rig." Advanced Materials Research 605-607 (December 2012): 1810–18. http://dx.doi.org/10.4028/www.scientific.net/amr.605-607.1810.

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The design of an intelligent controller based on fuzzy TS model for a pressure process rig is presented. The proposed controller consists of a fuzzy TS model, a feedback fuzzy TS model, and a low pass filter combined in an internal model control structure. The identification of the fuzzy TS model uses fuzzy clustering technique to mimic the nonlinearity characteristic of the process. Instead of least-squares algorithm, the instrumental variable method is used to estimate the consequent parameters of the fuzzy TS model in order to avoid inconsistency problem. The identified model is validated with the performance indicators variance-accounted-for and root mean square. By using the technique of inverse fuzzy model analytically, the feedback fuzzy controller is designed based on the identified fuzzy TS model. The performance of the proposed controller is verified through experiments at various operating points.

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24

Bouzbida, Mohamed, Lassad Hassine, and Abdelkader Chaari. "Robust Kernel Clustering Algorithm for Nonlinear System Identification." Mathematical Problems in Engineering 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/2427309.

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In engineering field, it is necessary to know the model of the real nonlinear systems to ensure its control and supervision; in this context, fuzzy modeling and especially the Takagi-Sugeno fuzzy model has drawn the attention of several researchers in recent decades owing to their potential to approximate nonlinear behavior. To identify the parameters of Takagi-Sugeno fuzzy model several clustering algorithms are developed such as the Fuzzy C-Means (FCM) algorithm, Possibilistic C-Means (PCM) algorithm, and Possibilistic Fuzzy C-Means (PFCM) algorithm. This paper presents a new clustering algorithm for Takagi-Sugeno fuzzy model identification. Our proposed algorithm called Robust Kernel Possibilistic Fuzzy C-Means (RKPFCM) algorithm is an extension of the PFCM algorithm based on kernel method, where the Euclidean distance used the robust hyper tangent kernel function. The proposed algorithm can solve the nonlinear separable problems found by FCM, PCM, and PFCM algorithms. Then an optimization method using the Particle Swarm Optimization (PSO) method combined with the RKPFCM algorithm is presented to overcome the convergence to a local minimum of the objective function. Finally, validation results of examples are given to demonstrate the effectiveness, practicality, and robustness of our proposed algorithm in stochastic environment.

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25

ANDRECUT, M., and M. K. ALI. "COMPETITIVE LEARNING OF FUZZY MODELS." International Journal of Modern Physics B 16, no. 30 (2002): 4621–39. http://dx.doi.org/10.1142/s0217979202014863.

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In general, fuzzy modeling requires two stages: structure identification (generating the fuzzy rule base) and parameter learning (optimizing parameters in fuzzy rules). Here, we present an on-line algorithm for competitive learning and optimization of fuzzy models. Differing from existing methods, in this approach the structure identification and parameter optimization of the fuzzy model can be carried out automatically, using on-line acquisition of data. We demonstrate this approach by applying it to different types of nonlinear system modeling.

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26

Wang, Tie, Jing Wu, Xin Zhang, and Jing Shang. "ABS Road Surface Fuzzy Discrimination Based on Normal and Exponential Distribution." Advanced Materials Research 433-440 (January 2012): 2743–48. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.2743.

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The methods of work this identification system is that distill wheel speed characteristics to standard signal then stored in the system. When the first anti-lock loop begin to input the initial velocity to identification model then extract theoretical value of the system and input the actual speed then calculate the similar with theoretical speed. The maximum similarity value is the identification when the pavement is true. After that every cycle identifies monitoring (again identify based on new data) that is establishing state observer. Though it is inaccurate due to various interference identifications or when the adhesion coefficient of pavement has changed ABS will determine quickly make the work more reasonable. Choose normal distribution and exponential distribution of statistical principles to identify the pavement and modify the identification model with gamma membership function. Tire model choose H.B.Pacejka model suitable for programming. It will use Gauss numerical integration and rational interpolation to increase the timeliness of identification. The method this paper described is suitable for the identification of low road.

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27

Du, Xin Hui, Shu Niu, Xing Min Wang, and Pu Hui Wang. "Study on the Modeling of Main Mine Ventilator Based on Artificial Intelligence." Applied Mechanics and Materials 130-134 (October 2011): 3526–30. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.3526.

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The parameters of main ventilator in mine such as air flow, wind speed, gas concentration and other conditions are closely related, for its complexity, it’s difficult to establish the nonlinear mathematic model, and it’s hard describe the model properties by traditional identification method. Neural network and Fuzzy system are used in mine main ventilator model identification. A Neural network based on RBF is used in neural network, and a T-S fuzzy model based on triangle membership function is used in Fuzzy identification. The simulation results show that the two methods can satisfy the needs of identification precision, convergence rate, stability and tracking ability simultaneous.

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28

Osadchyi, V. V., V. S. Yeremeev, A. V. Matsyura, and K. Jankowski. "Cluster analysis, fuzzy sets, and fuzzy logic models in bird identification." Ukrainian Journal of Ecology 7, no. 2 (2017): 96–103. http://dx.doi.org/10.15421/2017_25.

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<p>In our resent research (Osadchiy at al., 2016) we considered the mathematical model for the identifying of bird species according to the results of inaccurate field measurements. We used the total length of the bird, the wingspan, the wingbeat frequency, and the flight as the input factors of the model. Testing the model on a hypothetical case of identifying some target species, like Rook, Common raven, Mallard, White Stork, and Lapwing revealed that this model can be used for bird species identification with definite limitations. However, in previous model we applied the recognition algorithm that was based on the classical sections of mathematical statistics. The limitations of those model are obvious - it does not take into account many characteristics and behavioral features of birds that cannot be represented in numerical form, like diurnal activity pattern and flocking behavior. In this case the possibility of using the traditional sections of mathematical statistics is quite limited. The present study is devoted to the development of a mathematical method for the identifying of the bird species that based on cluster analysis with fuzzy logic and fuzzy sets which extends the possibilities of the algorithm that was previously proposed in our research.</p>

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29

Bansal, Neety, and Parvinder Kaur. "A Novel Approach to Fuzzy Model Identification Based on Bat Algorithm." International Journal of Applied Metaheuristic Computing 10, no. 2 (2019): 93–108. http://dx.doi.org/10.4018/ijamc.2019040104.

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The identification of a fuzzy model is a complex and nonlinear problem. This can be formulated as a search and optimisation problem and many computing approaches are available in the literature to solve this problem. This research paper is focused on using a new nature inspired approach for fuzzy modeling based on Bat Algorithm which is derived from the behaviour of micro-bats to search for their prey. The bat algorithm approach has been implemented and validated successfully on a rapid battery charger fuzzy controller problem. Currently, the key requirement is real-time solutions to complex problems at a blazing speed. Bat algorithm evolved the optimised fuzzy model within a few seconds as compared to other approaches.

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30

Andreeva, O. N., and E. V. Kurnasov. "Fuzzy Cognitive Model for Identification of Destabilizing Factors." Russian Engineering Research 39, no. 5 (2019): 399–406. http://dx.doi.org/10.3103/s1068798x19050034.

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31

Abdelazziz, Aouiche, Aouiche El Moundher, and Guiza Dhaouadi. "Efficient Neuro-Fuzzy Identification Model for Electrocardiogram Signal." Journal Européen des Systèmes Automatisés 55, no. 2 (2022): 237–44. http://dx.doi.org/10.18280/jesa.550211.

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This paper addresses the performance of the Artificial Neural Networks (ANNs), Fuzzy inference systems (FISs) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for the identification of some nonlinear systems with certain degree of uncertainty. The efficiency of the suggested methods in modeling and identification the responses were analyzed and compared. The Back-propagation algorithm and Takagi-Sugeno (TS) approach are used to train the ANNs, FISs and ANFIS, respectively. In this study we will show how ANFIS can be put in order to form nets that able to train from external data and information compared to ANNs and FISs. In order, it is proposed forms of inputs that can be used along with ANNs, FISs and ANFIS to modeling nonlinear systems. Two nonlinear systems with an electrocardiogram (ECG) signal in the form of simulation and complexity were used to test the identification of the structure presented. Because ANFIS has an inherent capacity to approximate unknown functions and to adjust the changes in inputs and parameters, it can be used to identify the proposed systems with a very high level of complexity. The results show that the ANFIS technique can provide the most ideal approximation when the right structures are employed.

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32

Saad Saoud, Lyes, Fayçal Rahmoune, Victor Tourtchine, and Kamel Baddari. "Generalized dynamical fuzzy model for identification and prediction." Journal of Intelligent & Fuzzy Systems 26, no. 4 (2014): 1771–85. http://dx.doi.org/10.3233/ifs-130856.

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33

Baruch, Ieroham, and Elena Gortcheva. "Fuzzy-Neural Model for Nonlinear Systems Identification 1." IFAC Proceedings Volumes 31, no. 4 (1998): 247–52. http://dx.doi.org/10.1016/s1474-6670(17)42166-5.

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34

Liu, Haifa, and Qingfu Wei. "Identification and optimization of T-S fuzzy model." IFAC Proceedings Volumes 32, no. 2 (1999): 5439–44. http://dx.doi.org/10.1016/s1474-6670(17)56926-8.

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35

Li, Chaoshun, Jianzhong Zhou, Xiuqiao Xiang, Qingqing Li, and Xueli An. "T–S fuzzy model identification based on a novel fuzzy c-regression model clustering algorithm." Engineering Applications of Artificial Intelligence 22, no. 4-5 (2009): 646–53. http://dx.doi.org/10.1016/j.engappai.2009.02.003.

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36

Beyhan, Selami, and Musa Alci. "Fuzzy functions based ARX model and new fuzzy basis function models for nonlinear system identification." Applied Soft Computing 10, no. 2 (2010): 439–44. http://dx.doi.org/10.1016/j.asoc.2009.08.015.

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37

Li, Xi, and Dan Feng Feng. "Study on Fuzzy Cluster in the Model for Rapid Identification of NC Processing." Key Engineering Materials 522 (August 2012): 673–76. http://dx.doi.org/10.4028/www.scientific.net/kem.522.673.

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In this paper, a application of signal identification by using fuzzy cluster is studied Based on the one order T-S model, an algorithm for online establishment the nonlinear model between the servo current and cutting force is presented by fuzzy likelihood function to derive fuzzy cluster. Finally, the experimental study has been given. The result showed that it can be regarded as a good dynamic identification algorithm for intelligent control of NC processing.

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38

Siddikov, Isamidin, Yusufjon Mamasodikov, Nodira Mamasodikova, and Ulugbek Khujanazarov. "Methods for optimizing data processing based on fuzzy adjustment of time series elements and identification model variables." E3S Web of Conferences 452 (2023): 03010. http://dx.doi.org/10.1051/e3sconf/202345203010.

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The problem of optimal identification and processing of random time series (SVR) based on the properties of statistical, dynamic, and fuzzy models is formulated. A method for qualitative identification of SVR is proposed, which includes algorithms for fuzzy equations, logical inferences, taking into account the effects of environmental factors and non-stationary processes. A generalized algorithm for identifying SVR with adjustment and correction of variable values based on the rules of fuzzy logic, methods for searching for extrema by t -norms and s -norms is developed. Tools are designed for optimal data processing by determining an adequate model; parametric and structural identification of objects; search optimization; model training; identification of the “input and output” relationship; formation and use of a knowledge base, as well as sets of fuzzy rules, linguistic variables, membership functions, and algorithms for regulating variable values. Methods of fuzzy correction of distorted information by controlling the error of SVR identification are developed, and a software package is implemented that provides high accuracy of data processing with significantly lower costs.

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39

Wang, Zhi Yong, and Yi Geng Li. "Rolling Force Prediction Method Based on Fuzzy Identification." Applied Mechanics and Materials 229-231 (November 2012): 365–68. http://dx.doi.org/10.4028/www.scientific.net/amm.229-231.365.

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To improve the precision and efficiency of rolling force prediction on hot rolled strip, a new rolling load prediction of finishing stands method was set up by fuzzy identification. It was based on T-S fuzzy model using clustering subjection functions to calculate the grade of membership for each given pattern, and using recursive least squares method to identify the consequent parameters of fuzzy model. On the basis of the measured data of the 1580 mm, the relation between the main hot strip mill parameters and rolling force was established using fuzzy model. Experimental results show that the prediction precision is higher, responds quickly and steady. The method can satisfy on line control requirements in a hot mill strip rolling process.

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40

Haj Hamad, I., A. Chouchaine, and H. Bouzaouache. "A Takagi-Sugeno Fuzzy Model for Greenhouse Climate." Engineering, Technology & Applied Science Research 11, no. 4 (2021): 7424–29. http://dx.doi.org/10.48084/etasr.4291.

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This paper investigates the identification and modeling of a greenhouse's climate using real climate data from a greenhouse installed in the LAPER laboratory in Tunisia. The objective of this paper is to propose a solution to the problem of nonlinear time-variant inputs and outputs of greenhouse internal climate. Combining fuzzy logic technique with Least Mean Squares (LMS), a robust greenhouse climate model for internal temperature prediction is proposed. The simulation results demonstrate the effectiveness of the identification approach and the power of the implemented Takagi-Sugeno Fuzzy model-based algorithm.

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41

Yin, Baoji, Feng Yao, Yujia Wang, Mingjun Zhang, and Chenguang Zhu. "Fault degree identification method for thruster of autonomous underwater vehicle using homomorphic membership function and low frequency trend prediction." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 233, no. 4 (2018): 1426–40. http://dx.doi.org/10.1177/0954406218768830.

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This article presents a novel thruster fault degree identification method for autonomous underwater vehicle. The novel method is developed from the fuzzy support vector domain description method, which establishes a fault identification model first, and then estimates fault degree according to the model. When establishing fault identification model for thruster based on fuzzy support vector domain description method, it is found that the relative fitting error of the model to the actual fault degree is large, making the model accuracy poor. To reduce the relative fitting error, a homomorphic membership function method is proposed. Different from fuzzy support vector domain description method, which calculates the fuzzy membership degree of fault sample in time domain, the proposed method calculates the fuzzy membership degree in log domain. On estimating thruster fault degree by fuzzy support vector domain description method, it is obtained that the estimated fault degree lags behind the actual fault degree. To shorten the lag time, a low frequency trend prediction method is proposed. Different from fuzzy support vector domain description method, which brings the fault feature extracted from the current surge speed and control voltage into the fault identification model to calculate fault degree, the proposed method firstly forward predicts surge speed and control voltage, and then takes the fault feature extracted from the predicted surge speed and control voltage into the model to acquire fault degree. The effectiveness of the proposed methods is verified by pool experiments of the experimental prototype.

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Ren, Yuan, Zhi Dan Zhong, Hong Xiao Liu, and Xiao Hui Wang. "Particle Swarm Optimization for Identification of PEMFC Generation System Fuzzy Model." Advanced Materials Research 588-589 (November 2012): 260–63. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.260.

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This paper proposes particle swarm optimization (PSO) for identification of the Proton Exchange Membrane Fuel Cells (PEMFC) generation systems fuzzy model. The PEM fuel cell generation system efficiency decreases as its output power increases. Thus, an optimum efficiency should exist and should result in a cost-effective PEM fuel cell generation system. The PEMFC generation system cost and efficiency fuzzy model were build, we use the PSO as an optimization engine to indentify the fuzzy model. The simulation results were presented and the results show that we may minimize the total cost of the generation system by using the PSO.

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NGUYEN, ERIC M., and NADIPURAM R. PRASAD. "MODEL IDENTIFICATION OF A SERVO-TRACKING SYSTEM USING FUZZY CLUSTERING." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 07, no. 04 (1999): 337–46. http://dx.doi.org/10.1142/s0218488599000295.

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This paper investigates the use of Fuzzy Clustering as a means for model identification of a complex and highly non-linear servo-tracking system when only observational data is available. The use of Fuzzy Clustering facilities automatic generation of rules and its antecedent parameters. The consequent of the model is then formulated in the form of Takagi, Sugeno and Kang (TSK), and its parameters determined by the Least Squares Method (LSM).

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Abdelaziz, Aouiche, Aouiche El Moundher, Aouiche Chaima, and Djellab Hanane. "Identification of Photovoltaic Panel MPPT Using Neuro-Fuzzy Model." European Journal of Electrical Engineering 24, no. 5-6 (2022): 273–79. http://dx.doi.org/10.18280/ejee.245-606.

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A photovoltaic (PV) panel produces energy that is influenced by external factors including temperature, irradiation, and the fluctuations in the load related to it. The PV system should perform at maximum power point (MPP) in order to adjust towards the rapidly increasing interest in energy. Because of the changing climatic conditions, it becomes has a limited efficiency. In order to maximize the PV system's efficiency, a maximum power point technique is necessary. In the present paper a maximum power point (MPP) of photovoltaic (PV) panel is designed and simulated to optimize system performance, accurate synthesis model based on the hybrid neural fuzzy systems is proposed to directly obtain the MPP. So, photovoltaic panel (PV) is analyzed with the mathematical model to obtain the training data. Three cases were used to test the identification of the structure proposed. The results show neuro-fuzzy (Sugeno Model) used were efficient in modeling the MPP of our PV panel. The Mean square error (MSE) is used as the fitness function to guarantee that the MSE is small, the algorithm synthesis model is validated by the MPP PV Panel analysis, simulation, and measurements. Neuro-fuzzy models is presented throughout this paper to demonstrate the effectiveness of the method of training suggested.

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Zhi, Chuan, Zhi Jian Li, and Yi Shi. "Research on Robustness of Color Device Characteristic Methods Based on Artificial Intelligence." Applied Mechanics and Materials 262 (December 2012): 65–68. http://dx.doi.org/10.4028/www.scientific.net/amm.262.65.

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The nature of device color characteristic methods is the mutual conversion of device-dependent color space and device-independent color space. This paper does the comparative study on the robustness of some color space conversion methods which are based on fuzzy control, dynamic subspace divided BP neural network identification method, and fuzzy and neural identification method, by defining the robustness of color space conversion model and evaluation method. The result shows that the device color characteristic methods which are based on fuzzy and neural identification method can make the feature of BP neural network combine with fuzzy control to greatly improve the robustness of model.

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Chen, Bin, Ge Liu, and Xian Ming Zhang. "Study on Properties of HVAS Waste-Oil Equipment Coating." Applied Mechanics and Materials 148-149 (December 2011): 50–53. http://dx.doi.org/10.4028/www.scientific.net/amm.148-149.50.

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Takagi-Sugeno (T_S) fuzzy model of abrasion resistance of HVAS coating and technological parameters is proposed. The results of identification and simulation of the model show that the identified fuzzy model has relatively high precision and good generalization ability; the identification method is valid. By the use of the model, analyzed influence on properties of HVAS coating with technological parameters.

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Meng, Juan, Hai Du, and Xing Yuan Wang. "Adaptive Fuzzy Synchronization Design and Parameter Identification for Chaotic Systems." Applied Mechanics and Materials 644-650 (September 2014): 2514–21. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2514.

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In this paper, a new fuzzy model-based adaptive approach for synchronization of chaotic systems with unknown parameters. Theoretical analysis based on Lyapunov stability theory is provided to verify its feasibility. Takagi-Sugeno (T-S) fuzzy model is employed to express the chaotic systems. Based on this model, an adaptive fuzzy controller and the parameters update law are developed. With the proposed scheme, parameters identification and synchronization of identical or nonidentical chaotic systems can be achieved simultaneously. Numerical simulations further demonstrate the effectiveness of the proposed scheme.

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Mengall, G. "Fuzzy modelling for aircraft dynamics identification." Aeronautical Journal 105, no. 1051 (2001): 551–55. http://dx.doi.org/10.1017/s0001924000018029.

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A new methodology is described to identify aircraft dynamics and extract the corresponding aerodynamic coefficients. The proposed approach makes use of fuzzy modelling for the identification process where input/output data are first classified by means of the concept of fuzzy clustering and then the linguistic rules are extracted from the fuzzy clusters. The fuzzy rule-based models are in the form of affine Takagi-Sugeno models, that are able to approximate a large class of nonlinear systems. A comparative study is performed with existing techniques based on the employment of neural networks, showing interesting advantages of the proposed methodology both for the physical insight of the identified model and the simplicity to obtain accurate results with fewer parameters to be properly tuned.

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Zeng, Yongping, Yongyi Yan, Shun Weng, Yanhua Sun, Wei Tian, and Hong Yu. "Fuzzy clustering of time-series model to damage identification of structures." Advances in Structural Engineering 22, no. 4 (2018): 868–81. http://dx.doi.org/10.1177/1369433218789191.

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Time-series methods have been popularly used for damage identification of civil structure because of its output-only and non-model approach. Since the existence of structural damage is usually vague and not focussed on any particular time point, the switches in damage patterns from one time state to another are necessary to be treated in a fuzzy way. This article develops a damage identification method based on the fuzzy clustering of time-series model. The changes of model coefficients of time-series model are proposed to indicate the undamaged and damaged states by the fuzzy c-means clustering algorithm. The residual errors of time-series model are used to identify the damage location and damage severity. The proposed method is applied to an experimental segment lining and a numerical study of a practical bridge. The results verify that the proposed method is accurate and efficient to detect the structural damage location and severity. Since the computational process of time-series model and fuzzy clustering require low computational cost, the proposed data-based damage identification method is applicable to the online structural health monitoring system of large-scale civil structures.

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Campello, R. J. G. B., R. M. Nazzetta, and W. C. do Amaral. "A Highly Adaptive Algorithm for Fuzzy Modelling of Systems." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 06, no. 01 (1998): 35–50. http://dx.doi.org/10.1142/s0218488598000033.

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An optimization-based methodology for on-line discrete-time fuzzy relational model identification is proposed. Approximated solutions for extended fuzzy relational equations are derived without the necessity of the previous identification of an appropriate initial fuzzy relation. A heuristic is used making the algorithm capable of solving the identification problem when the fuzzy relational matrix is biased. A set of quadratic performance indices containing only fuzzy quantities provides accurate results and allows the utilization of a simple optimization method. The efficiency of the proposed methodology is illustrated using two numerical examples in which synthetic data are generated by the identified models.

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