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Artículos de revistas sobre el tema "Fuzzy neural networks"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 25 de julio de 2025

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1

Rao, D. H. "Fuzzy Neural Networks." IETE Journal of Research 44, no. 4-5 (1998): 227–36. http://dx.doi.org/10.1080/03772063.1998.11416049.

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2

ISHIBUCHI, Hisao, Hidehiko OKADA, and Hideo TANAKA. "Fuzzy Neural Networks with Fuzzy Weights." Transactions of the Institute of Systems, Control and Information Engineers 6, no. 3 (1993): 137–48. http://dx.doi.org/10.5687/iscie.6.137.

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3

Geng, Z. Jason. "Fuzzy CMAC Neural Networks." Journal of Intelligent and Fuzzy Systems 3, no. 1 (1995): 87–102. http://dx.doi.org/10.3233/ifs-1995-3108.

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4

Dunyak, James, and Donald Wunsch. "Fuzzy number neural networks." Fuzzy Sets and Systems 108, no. 1 (1999): 49–58. http://dx.doi.org/10.1016/s0165-0114(97)00339-4.

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5

Md., Musa Khan. "Comparison of Selection Method of a Membership Function for Fuzzy Neural Networks." International Journal of Case Studies 6, no. 11 (2017): 71–77. https://doi.org/10.5281/zenodo.3538605.

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Fuzzy neural networks are learning machine that realize the parameters of a fuzzy system (i.e., fuzzy sets, fuzzy rules) by exploiting approximation techniques from neural networks. In this paper, we tend to illustrate a general methodology, based on statistical analysis of the training data, for the choice of fuzzy membership functions to be utilized in reference to fuzzy neural networks. Fuzzy neural networks give for the extraction of fuzzy rules for from artificial neural network architectures. First, the technique is represented and so illustrated utilizing two experimental examinations f

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6

Yang, Xin. "Quantum fuzzy neural network based on fuzzy number." Frontiers in Computing and Intelligent Systems 3, no. 2 (2023): 99–105. http://dx.doi.org/10.54097/fcis.v3i2.7524.

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Neural network is one of the AI algorithms commonly used to process data, and has an extremely important position in scenarios such as image recognition, classification, and machine translation. With the increase of data volume explosion, the required computing power of neural networks is also significantly increased. The emergence of quantum neural networks improves the computational power of neural networks, but the accuracy of neural networks and quantum neural networks is not high in the face of the complexity and uncertainty of big data. In order to improve the efficiency and accuracy, th

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7

Purushothaman, G., and N. B. Karayiannis. "Quantum neural networks (QNNs): inherently fuzzy feedforward neural networks." IEEE Transactions on Neural Networks 8, no. 3 (1997): 679–93. http://dx.doi.org/10.1109/72.572106.

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8

Blake, J. "The implementation of fuzzy systems, neural networks and fuzzy neural networks using FPGAs." Information Sciences 112, no. 1-4 (1998): 151–68. http://dx.doi.org/10.1016/s0020-0255(98)10029-4.

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9

Dunyak, James P., and Donald Wunsch. "Fuzzy regression by fuzzy number neural networks." Fuzzy Sets and Systems 112, no. 3 (2000): 371–80. http://dx.doi.org/10.1016/s0165-0114(97)00393-x.

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10

Mosleh, M., M. Otadi, and S. Abbasbandy. "Fuzzy polynomial regression with fuzzy neural networks." Applied Mathematical Modelling 35, no. 11 (2011): 5400–5412. http://dx.doi.org/10.1016/j.apm.2011.04.039.

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11

OH, SUNG-KWUN, DONG-WON KIM, and WITOLD PEDRYCZ. "HYBRID FUZZY POLYNOMIAL NEURAL NETWORKS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10, no. 03 (2002): 257–80. http://dx.doi.org/10.1142/s0218488502001478.

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We propose a hybrid architecture based on a combination of fuzzy systems and polynomial neural networks. The resulting Hybrid Fuzzy Polynomial Neural Networks (HFPNN) dwells on the ideas of fuzzy rule-based computing and polynomial neural networks. The structure of the network comprises of fuzzy polynomial neurons (FPNs) forming the nodes of the first (input) layer of the HFPNN and polynomial neurons (PNs) that are located in the consecutive layers of the network. In the FPN (that forms a fuzzy inference system), the generic rules assume the form "if A then y = P(x) " where A is fuzzy relation

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12

Thakur, Amey. "Neuro-Fuzzy: Artificial Neural Networks & Fuzzy Logic." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (2021): 128–35. http://dx.doi.org/10.22214/ijraset.2021.37930.

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Abstract: Neuro Fuzzy is a hybrid system that combines Artificial Neural Networks with Fuzzy Logic. Provides a great deal of freedom when it comes to thinking. This phrase, on the other hand, is frequently used to describe a system that combines both approaches. There are two basic streams of neural network and fuzzy system study. Modelling several elements of the human brain (structure, reasoning, learning, perception, and so on) as well as artificial systems and data: pattern clustering and recognition, function approximation, system parameter estimate, and so on. In general, neural networks

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13

Reddy, Bapatu Siva Kumar, and P. Vishnu Vardhan. "Novel Alphabet Deduction Using MATLAB by Neural Networks and Comparison with the Fuzzy Classifier." Alinteri Journal of Agriculture Sciences 36, no. 1 (2021): 623–28. http://dx.doi.org/10.47059/alinteri/v36i1/ajas21088.

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Aim: The study aims to identify or recognize the alphabets using neural networks and fuzzy classifier/logic. Methods and materials: Neural network and fuzzy classifier are used for comparing the recognition of characters. For each classifier sample size is 20. Character recognition was developed using MATLAB R2018a, a software tool. The algorithm is again compared with the Fuzzy classifier to know the accuracy level. Results: Performance of both fuzzy classifier and neural networks are calculated by the accuracy value. The mean value of the fuzzy classifier is 82 and the neural network is 77.

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14

Rutkowska, Danuta, and Yoichi Hayashi. "Neuro-Fuzzy Systems Approaches." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 3 (1999): 177–85. http://dx.doi.org/10.20965/jaciii.1999.p0177.

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Two major approaches to neuro-fuzzy systems are distinguished in the paper. The previous one refers to fuzzy neural networks, which are neural networks with fuzzy signals, and/or fuzzy weights, as well as fuzzy transfer functions. The latter approach concerns neuro-fuzzy systems in the form of multilayer feed-forward networks, which differ from standard neural networks, because elements of particular layers conduct different operations than standard neurons. These structures are neural network representations of fuzzy systems and they are also called connectionist models of fuzzy systems, adap

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15

Ahn, Choon Ki. "Stability Conditions for Fuzzy Neural Networks." Advances in Fuzzy Systems 2012 (2012): 1–4. http://dx.doi.org/10.1155/2012/281821.

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This paper presents a novel approach to assess the stability of fuzzy neural networks. First, we propose a new condition for the stability of fuzzy neural networks. Second, a new stability condition based on linear matrix inequality (LMI) is presented for fuzzy neural networks. These conditions also ensure asymptotic stability without external input.

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16

Pedrycz, Witold. "Logic - Oriented Fuzzy Neural Networks." International Journal of Hybrid Intelligent Systems 1, no. 1-2 (2004): 3–11. http://dx.doi.org/10.3233/his-2004-11-203.

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17

DENG, Zhao-Hong. "Robust Fuzzy Clustering Neural Networks." Journal of Software 16, no. 8 (2005): 1415. http://dx.doi.org/10.1360/jos161415.

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18

Godjevac, Jelena, and Nigel Steele. "Fuzzy Systems and Neural Networks." Intelligent Automation & Soft Computing 4, no. 1 (1998): 27–37. http://dx.doi.org/10.1080/10798587.1998.10750719.

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19

Kosko, Bart, and John C. Burgess. "Neural Networks and Fuzzy Systems." Journal of the Acoustical Society of America 103, no. 6 (1998): 3131. http://dx.doi.org/10.1121/1.423096.

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20

Pedrycz, Witold. "Fuzzy neural networks and neurocomputations." Fuzzy Sets and Systems 56, no. 1 (1993): 1–28. http://dx.doi.org/10.1016/0165-0114(93)90181-g.

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21

Buckley, James J., and Yoichi Hayashi. "Fuzzy neural networks: A survey." Fuzzy Sets and Systems 66, no. 1 (1994): 1–13. http://dx.doi.org/10.1016/0165-0114(94)90297-6.

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22

Virgil Negoita, Constantin. "Neural Networks as Fuzzy Systems." Kybernetes 23, no. 3 (1994): 7–9. http://dx.doi.org/10.1108/03684929410059000.

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Any fuzzy system is a knowledge‐based system which implies an inference engine. Proposes neural networks as a means of performing the inference. Using the Theorem of Representation proposes an encoding scheme that allows the neural network to be trained to perform modus ponens.

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23

Dvorak, V. "Neural networks and fuzzy systems." Knowledge-Based Systems 6, no. 3 (1993): 179. http://dx.doi.org/10.1016/0950-7051(93)90043-s.

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24

Zambelli, Stefano. "Neural networks and fuzzy systems." Journal of Economic Dynamics and Control 17, no. 3 (1993): 523–29. http://dx.doi.org/10.1016/0165-1889(93)90010-p.

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25

Aliev, R. A., B. G. Guirimov, Bijan Fazlollahi, and R. R. Aliev. "Evolutionary algorithm-based learning of fuzzy neural networks. Part 2: Recurrent fuzzy neural networks." Fuzzy Sets and Systems 160, no. 17 (2009): 2553–66. http://dx.doi.org/10.1016/j.fss.2008.12.018.

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26

NASRABADI, EBRAHIM, and S. MEHDI HASHEMI. "ROBUST FUZZY REGRESSION ANALYSIS USING NEURAL NETWORKS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16, no. 04 (2008): 579–98. http://dx.doi.org/10.1142/s021848850800542x.

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Some neural network related methods have been applied to nonlinear fuzzy regression analysis by several investigators. The performance of these methods will significantly worsen when the outliers exist in the training data set. In this paper, we propose a training algorithm for fuzzy neural networks with general fuzzy number weights, biases, inputs and outputs for computation of nonlinear fuzzy regression models. First, we define a cost function that is based on the concept of possibility of fuzzy equality between the fuzzy output of fuzzy neural network and the corresponding fuzzy target. Nex

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27

Apiecionek, Lukasz. "Fuzzy Neural Networks—A Review with Case Study." Applied Sciences 15, no. 13 (2025): 6980. https://doi.org/10.3390/app15136980.

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This publication focuses on the use of fuzzy neural networks for data prediction. The author reviews papers in which fuzzy neural networks were used. The papers were selected mainly from 2020 to 2025 and were selected if fuzzy neural network were used for practical applications. Also, some chosen networks are described: FALCON, ANFIS, and a fuzzy network with Ordered Fuzzy Numbers. The networks with the implementation code presented in other publications were tested and compared to K Neighbors Classifier, Decision Tree Classifier, and Random Forest Classifier. The methodology and configuration

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28

ISHIBUCHI, Hisao. "Neural Networks with Fuzzy Inputs and Fuzzy Outputs." Journal of Japan Society for Fuzzy Theory and Systems 5, no. 2 (1993): 218–32. http://dx.doi.org/10.3156/jfuzzy.5.2_218.

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29

Otadi, Mahmood. "Fully fuzzy polynomial regression with fuzzy neural networks." Neurocomputing 142 (October 2014): 486–93. http://dx.doi.org/10.1016/j.neucom.2014.03.048.

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30

Lee, Keon-Myung, Dong-Hoon Kwakb, and Hyung Leekwang. "Tuning of fuzzy models by fuzzy neural networks." Fuzzy Sets and Systems 76, no. 1 (1995): 47–61. http://dx.doi.org/10.1016/0165-0114(95)00027-i.

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31

Aliev, Rafik A., Bijan Fazlollahi, and Rustam M. Vahidov. "Genetic algorithm-based learning of fuzzy neural networks. Part 1: feed-forward fuzzy neural networks." Fuzzy Sets and Systems 118, no. 2 (2001): 351–58. http://dx.doi.org/10.1016/s0165-0114(98)00461-8.

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32

Anil Kumar Chikatimarla, S. Prabhavathi, and A. Naga Srinivasa Rao. "A Comparative Study of Fuzzy Logic and Neural Networks for Pattern Recognition." Integral Research 2, no. 3 (2025): 129–35. https://doi.org/10.57067/ir.v2i3.242.

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This study investigates the difficulties of pattern identification using fuzzy logic and neural networks. Pattern recognition is crucial in numerous fields, including data science, computer vision, and voice recognition. Both neural networks and fuzzy logic, the two most used methods, have their advantages and disadvantages. Students will go extensively into neural networks and fuzzy logic after a brief review of math fundamentals. Speed, pattern recognition, and rapid learning are the three pillars upon which the ideology of a master designer rests. Professionals frequently employ publicly ac

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33

Ma, Yunlong, Tao Xie, and Yijia Zhang. "Robustness analysis of neutral fuzzy cellular neural networks with stochastic disturbances and time delays." AIMS Mathematics 9, no. 10 (2024): 29556–72. http://dx.doi.org/10.3934/math.20241431.

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<p>This paper discusses the robustness of neutral fuzzy cellular neural networks with stochastic disturbances and time delays. This work questions whether fuzzy cellular neural networks, which initially remains stable, can be stabilised again when the system is subjected to three simultasneous perturbations i.e., neutral items, random disturbances, and time delays. First, by using inequality techniques such as Gronwall's Lemma, the Itŏ formula, and the property of integrals, the transcendental equations that contain the contraction coefficient of the neutral terms, the intensity of the r

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34

Gao, Fengyu, Jer-Guang Hsieh, Ying-Sheng Kuo, and Jyh-Horng Jeng. "Study on Resistant Hierarchical Fuzzy Neural Networks." Electronics 11, no. 4 (2022): 598. http://dx.doi.org/10.3390/electronics11040598.

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Novel resistant hierarchical fuzzy neural networks are proposed in this study and their deep learning problems are investigated. These fuzzy neural networks can be used to model complex controlled plants and can also be used as fuzzy controllers. In general, real-world data are usually contaminated by outliers. These outliers may have undesirable or unpredictable influences on the final learning machines. The correlations between the target and each of the predictors are utilized to partition input variables into groups so that each group becomes the input variables of a fuzzy system in each l

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35

Yu, Xin, Mian Xie, Li Xia Tang, and Chen Yu Li. "Learning Algorithm for Fuzzy Perceptron with Max-Product Composition." Applied Mechanics and Materials 687-691 (November 2014): 1359–62. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1359.

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Fuzzy neural networks is a powerful computational model, which integrates fuzzy systems with neural networks, and fuzzy perceptron is a kind of this neural networks. In this paper, a learning algorithm is proposed for a fuzzy perceptron with max-product composition, and the topological structure of this fuzzy perceptron is the same as conventional linear perceptrons. The inner operations involved in the working process of this fuzzy perceptron are based on the max-product logical operations rather than conventional multiplication and summation etc. To illustrate the finite convergence of propo

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36

Su, Hai Bin, and Zhi Chong Cheng. "A Maximum Power Point Tracking Algorithm Based on Fuzzy Neural Networks for Grid-Connected Photovoltaic System." Advanced Materials Research 291-294 (July 2011): 2771–74. http://dx.doi.org/10.4028/www.scientific.net/amr.291-294.2771.

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This paper proposes a algorithm of maximum power point tracking using fuzzy Neural Networks for grid-connected photovoltaic systems. The system is composed of a VSI converter, the maximum power point tracking algorithm based on fuzzy Neural Networks outputs a reference voltage as voltage loop import variable. The voltage controller outputs a reference current to control inverter output current in side grid. The fuzzy Neural Networks provide attractive features such as fast response, good performance. Therefore, the system is able to deliver energy to grid. This proposed algorithm is simulated

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37

Horikawa, Shin-ichi, Masahiro Yamaguchi, Takeshi Furuhashi, and Yoshiki Uchikawa. "Fuzzy Control for Inverted Pendulum Using Fuzzy Neural Networks." Journal of Robotics and Mechatronics 7, no. 1 (1995): 36–44. http://dx.doi.org/10.20965/jrm.1995.p0036.

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Fuzzy control has a distinctive feature in that it can incorporate experts' control rules using linguistic expressions. The authors have presented various types of fuzzy neural networks (FNNs) called Type I-V. The FNNs can automatically identify the fuzzy rules and tune the membership functions of fuzzy controllers by utilizing the learning capability of neural networks. In particular, the Type IV FNN has a simple structure and can express the identified fuzzy rules linguistically. The authors have also proposed a method to describe the behavior of fuzzy control systems based on the fuzzy mode

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38

Syed Ali, M., Gani Stamov, Ivanka Stamova, Tarek F. Ibrahim, Arafa A. Dawood, and Fathea M. Osman Birkea. "Global Asymptotic Stability and Synchronization of Fractional-Order Reaction–Diffusion Fuzzy BAM Neural Networks with Distributed Delays via Hybrid Feedback Controllers." Mathematics 11, no. 20 (2023): 4248. http://dx.doi.org/10.3390/math11204248.

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In this paper, the global asymptotic stability and global Mittag–Leffler stability of a class of fractional-order fuzzy bidirectional associative memory (BAM) neural networks with distributed delays is investigated. Necessary conditions are obtained by means of the Lyapunov functional method and inequality techniques. The hybrid feedback controllers are then developed to ensure the global asymptotic synchronization of these neural networks, resulting in two additional synchronization criteria. The derived conditions are applied to check the fractional-order fuzzy BAM neural network’s Mittag–Le

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39

Zhu, Jian Min, Peng Du, and Ting Ting Fu. "Research for RBF Neural Networks Modeling Accuracy of Determining the Basis Function Center Based on Clustering Methods." Advanced Materials Research 317-319 (August 2011): 1529–36. http://dx.doi.org/10.4028/www.scientific.net/amr.317-319.1529.

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The radial basis function (RBF) neural network is superior to other neural network on the aspects of approximation ability, classification ability, learning speed and global optimization etc., it has been widely applied as feedforward networks, its performance critically rely on the choice of RBF centers of network hidden layer node. K-means clustering, as a commonly method used on determining RBF center, has low neural network generalization ability, due to its clustering results are not sensitive to initial conditions and ignoring the influence of dependent variable. In view of this problem,

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40

Wang, Yang, Li Li Guo, and Chen Guo. "A Dynamic Fuzzy Neural Networks-Based Surface Vessels Course Tracking Controller." Applied Mechanics and Materials 644-650 (September 2014): 122–27. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.122.

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A Dynamic Fuzzy Neural Networks Course Tracking Controller (DFNNCTC) for Surface Vessels is presented to solve the uncertainties coursing by the wide and wave. A Dynamic Fuzzy Neural Networks (DFNN) combines with a PID controller to integrate the DFNNCTC, in which the structure and parameters are adjusted online, and the fuzzy rules are automatically generated when being trained. The intelligent algorithm conquers the disadvantage of either overfitting or overtraining in traditional static fuzzy neural networks-based control methods. Simulation results of a container’s course tracking control

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41

Chen, Yi-Chung. "Machine Monitoring Using Fuzzy-Neural Networks." International Journal of Automation and Smart Technology 8, no. 2 (2018): 73–78. http://dx.doi.org/10.5875/ausmt.v8i2.1686.

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42

Freitag, Steffen, Wolfgang Graf, and Michael Kaliske. "Recurrent neural networks for fuzzy data." Integrated Computer-Aided Engineering 18, no. 3 (2011): 265–80. http://dx.doi.org/10.3233/ica-2011-0373.

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43

Pedrycz, W., M. Reformat, and C. W. Han. "Cascade Architectures of Fuzzy Neural Networks." Fuzzy Optimization and Decision Making 3, no. 1 (2004): 5–37. http://dx.doi.org/10.1023/b:fodm.0000013070.26870.e6.

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44

MIZRAJI, EDUARDO, and JUAN LIN. "FUZZY DECISIONS IN MODULAR NEURAL NETWORKS." International Journal of Bifurcation and Chaos 11, no. 01 (2001): 155–67. http://dx.doi.org/10.1142/s0218127401002043.

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Modular neural networks structured as associative memories are capable of processing inputs built from tensorial products of vectors. In this context, the operators of propositional and modal logic can be represented as modular distributed memories that can process not only classical Boolean but also fuzzy evaluations of truth-values of sentences. Furthermore, projecting memory outputs onto unit vectors yield discrete dynamical systems that exhibit varying degrees of complexity. As examples, we analyze outcomes of semantic evaluations in several self-referential systems including modal version

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45

Senol, Canan, and Tulay Yildirim. "Fuzzy-neural networks for medical diagnosis." International Journal of Reasoning-based Intelligent Systems 2, no. 3/4 (2010): 265. http://dx.doi.org/10.1504/ijris.2010.036873.

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46

Leu, Yih-Guang, Tsu-Tian Lee, and Wei-Yen Wang. "Linearization Case of Fuzzy-Neural Networks." IFAC Proceedings Volumes 29, no. 1 (1996): 2496–501. http://dx.doi.org/10.1016/s1474-6670(17)58049-0.

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47

Oh, Sung-Kwun, Witold Pedrycz, and Ho-Sung Park. "Hybrid identification in fuzzy-neural networks." Fuzzy Sets and Systems 138, no. 2 (2003): 399–426. http://dx.doi.org/10.1016/s0165-0114(02)00441-4.

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48

Yang, Yupu, Xiaoming Xu, and Wenyuan Zhang. "Design neural networks based fuzzy logic." Fuzzy Sets and Systems 114, no. 2 (2000): 325–28. http://dx.doi.org/10.1016/s0165-0114(98)00098-0.

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49

Al-Daraiseh, Ahmad, Assem Kaylani, Michael Georgiopoulos, Mansooreh Mollaghasemi, Annie S. Wu, and Georgios Anagnostopoulos. "GFAM: Evolving Fuzzy ARTMAP neural networks." Neural Networks 20, no. 8 (2007): 874–92. http://dx.doi.org/10.1016/j.neunet.2007.05.006.

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50

Han, Man-Wook, and Peter Kopacek. "Neural Networks and Fuzzy Robot Control." IFAC Proceedings Volumes 30, no. 14 (1997): 291–96. http://dx.doi.org/10.1016/s1474-6670(17)42737-6.

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