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Journal articles on the topic 'Neural Fuzzy'

Author: Grafiati

Published: 3 June 2025

Last updated: 1 August 2025

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1

Sztandera, Les M. "Fuzzy neural trees." Information Sciences 90, no. 1-4 (1996): 157–77. http://dx.doi.org/10.1016/0020-0255(95)00242-1.

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2

Kruse, Rudolf. "Fuzzy neural network." Scholarpedia 3, no. 11 (2008): 6043. http://dx.doi.org/10.4249/scholarpedia.6043.

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3

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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4

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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5

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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6

Musilekl, Petr, and Madan M. Gupta. "Fuzzy Neural Models Based on Some New Fuzzy* Arithmetic Operations." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 4 (1999): 245–54. http://dx.doi.org/10.20965/jaciii.1999.p0245.

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This paper introduces a novel approach to fuzzy arithmetic computation in fuzzy neural networks. The first part provides an overview of the standard fuzzy arithmetic operations and limitations of their use in fuzzy arithmetic based neural models. Consequently, alternative fuzzy arithmetic operations are developed and their aspects for the neural models are discussed in more detail. Originality of our approach lies in the treatment of neural inputs and weights as interactive variables which allows control of uncertainty growth in neural processing. Besides the detailed theoretical description o

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7

Zamirpour, Ehsan, and Mohammad Mosleh. "A biological brain-inspired fuzzy neural network: Fuzzy emotional neural network." Biologically Inspired Cognitive Architectures 26 (October 2018): 80–90. http://dx.doi.org/10.1016/j.bica.2018.07.019.

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8

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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9

Zhang, Yong Chao, Wen Zhuang Zhao, and Jin Lian Chen. "The Research and Application of the Fuzzy Neural Network Control Based on Genetic Algorithm." Advanced Materials Research 403-408 (November 2011): 191–95. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.191.

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How fuzzy technology and neural networks and genetic algorithm combine with each other has become the focus of research. A fuzzy neural network controller was proposed based on defuzzification and optimization around the fuzzy neural network structure. Genetic algorithm of fuzzy neural network was brought forward based on optimal control theory. Optimal structure and parameters of fuzzy neural network controller were Offline searched by way of controller performance indicators of genetic algorithm. Fuzzy neural network controller through genetic algorithm was accessed in fuzzy neural network i

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10

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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11

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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12

Villmann, Th, B. Hammer, F. Schleif, T. Geweniger, and W. Herrmann. "Fuzzy classification by fuzzy labeled neural gas." Neural Networks 19, no. 6-7 (2006): 772–79. http://dx.doi.org/10.1016/j.neunet.2006.05.026.

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13

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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14

Li, Ye, Xiao Liu, Zhenliang Yang, et al. "Prediction Model for Geologically Complicated Fault Structure Based on Artificial Neural Network and Fuzzy Logic." Scientific Programming 2022 (March 10, 2022): 1–12. http://dx.doi.org/10.1155/2022/2630953.

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The development and distribution of geologically complicated fault structure have the characteristics of uncertainty, randomness, ambiguity, and variability. Therefore, the prediction of complicated fault structures is a typical nonlinear problem. Neither fuzzy logic method nor artificial neural network alone can solve this problem well because the fuzzy method is generally not easy to realize adaptive learning function, and the neural network method is not suitable for describing sedimentary microfacies or geophysical facies. Therefore, taking the marginal subsags in the Jiyang Depression, Ea

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15

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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16

Chen, Cheng-Hung, and Wen-Hsien Chen. "Symbiotic Particle Swarm Optimization for Neural Fuzzy Controllers." International Journal of Machine Learning and Computing 4, no. 5 (2014): 433–36. http://dx.doi.org/10.7763/ijmlc.2014.v4.450.

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17

Kanth, Dr B. B. M. Krishna. "A Fuzzy-Neural Approach for Leukemia Cancer Classification." International Journal of Scientific Research 2, no. 11 (2012): 206–8. http://dx.doi.org/10.15373/22778179/nov2013/66.

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18

Yerokhin, A. L., and O. V. Zolotukhin. "Fuzzy probabilistic neural network in document classification tasks." Information extraction and processing 2018, no. 46 (2018): 68–71. http://dx.doi.org/10.15407/vidbir2018.46.068.

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19

Su, Shun-Feng, and Jen-Wei Yeh. "On Neural Fuzzy Systems." International Journal of Fuzzy Logic and Intelligent Systems 14, no. 4 (2014): 276–87. http://dx.doi.org/10.5391/ijfis.2014.14.4.276.

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20

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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21

Eklund, P., and F. Klawonn. "Neural fuzzy logic programming." IEEE Transactions on Neural Networks 3, no. 5 (1992): 815–18. http://dx.doi.org/10.1109/72.159071.

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22

Nishina, Takatoshi, and Masafumi Hagiwara. "Fuzzy inference neural network." Neurocomputing 14, no. 3 (1997): 223–39. http://dx.doi.org/10.1016/s0925-2312(96)00036-7.

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23

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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24

Colin Johnson, R. "IEEE neural/fuzzy conference." Neurocomputing 5, no. 6 (1993): 311–17. http://dx.doi.org/10.1016/0925-2312(93)90046-6.

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25

Ciaramella, A., R. Tagliaferri, W. Pedrycz, and A. Di Nola. "Fuzzy relational neural network." International Journal of Approximate Reasoning 41, no. 2 (2006): 146–63. http://dx.doi.org/10.1016/j.ijar.2005.06.016.

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26

Hai-bo, Liu, Gu Guo-chang, Shen Jing, and Fu Yan. "AUV fuzzy neural BDI." Journal of Marine Science and Application 4, no. 3 (2005): 37–41. http://dx.doi.org/10.1007/s11804-005-0019-y.

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27

Zhan-Peng Cui, Zhan-Peng Cui. "Restoration and Enhancement of Fuzzy Defect Image Based on Neural Network." 電腦學刊 34, no. 4 (2023): 001–14. http://dx.doi.org/10.53106/199115992023083404001.

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<p>In contrast enhancement of fuzzy defect image, details loss and noise expansion are east to occur, which brings difficulties to subsequent image analysis and defect recognition. Therefore, a fuzzy defect image restoration and enhancement method based on neural network is proposed. A double fusion neural network composed of a depth generation network and a discrimination network is designed. The residual of the denoised fuzzy image and the real image is output by the network, which is input into the discrimination network together with the real image, and the difference between the two

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28

Li, Jie Jia, Yong Qiang Chen, and Xiao Yan Han. "Fuzzy Neural Network Based on Genetic Algorithm for Temperature Control of Variable Air Volume Air Conditioning." Applied Mechanics and Materials 599-601 (August 2014): 952–55. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.952.

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In this paper, the theory of the fuzzy control and self-learning ability of neural network is combined, joining the genetic algorithm to optimize the fuzzy control rules, so in the light of temperature control system of variable air volume air conditioning puts forward a fuzzy neural network control method based on genetic algorithm,and this paper introduces in detail the structure, algorithm of fuzzy control and neural network. In addition,this paper verifies the superiority of the fuzzy neural network based on genetic algorithm and ordinary fuzzy neural control.

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29

Shi, De Quan, Gui Li Gao, Ying Liu, Hui Ying Tang, and Zhi Gao. "Temperature Controller of Heating Furnace Based on Fuzzy Neural Network Technology." Advanced Materials Research 748 (August 2013): 820–25. http://dx.doi.org/10.4028/www.scientific.net/amr.748.820.

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In this study, to solve the problem that heating furnace has the disadvantage of non-linearity, time variant and large delay, a fuzzy neural network controller has been designed according to the combination of fuzzy control and neural networks. In this controller, not only can the reasoning process of neural network be described by the fuzzy rules, but also the fuzzy rules can be dynamically adjusted by the neural network. In addition, the learning algorithm of the fuzzy neural network controller is studied. Simulation results show that the fuzzy neural network controller has good regulating p

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30

Oh, June Yeol, Seong Nam Kang, Yong Jeong Huh, Hyun Chan Cho, Man Sung Choi, and Kwang Sun Kim. "A Study on Optimal Solution of Short Shot Using Fuzzy Logic Based Neural Network(FNN)(Neural Fuzzy Application,Session: MP2-C)." Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2004.4 (2004): 35. http://dx.doi.org/10.1299/jsmeicam.2004.4.35_1.

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31

Geweniger, Tina, Lydia Fischer, Marika Kaden, Mandy Lange, and Thomas Villmann. "Clustering by Fuzzy Neural Gas and Evaluation of Fuzzy Clusters." Computational Intelligence and Neuroscience 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/165248.

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We consider some modifications of the neural gas algorithm. First, fuzzy assignments as known from fuzzy c-means and neighborhood cooperativeness as known from self-organizing maps and neural gas are combined to obtain a basic Fuzzy Neural Gas. Further, a kernel variant and a simulated annealing approach are derived. Finally, we introduce a fuzzy extension of the ConnIndex to obtain an evaluation measure for clusterings based on fuzzy vector quantization.

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32

Gao, Shu Zhi, Jing Yang, and Jun Fan. "Modeling of Distillation Tower Temperature Based on D-FNN." Advanced Materials Research 383-390 (November 2011): 1463–69. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.1463.

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Distillation temperature control system is characteristics of nonlinear time-varying and we use dynamic fuzzy neural network to model the temperature of distillation. Firstly, we introduce the structure and algorithm of dynamic fuzzy neural network; Second, after data preprocessing of distillation process, we use dynamic Fuzzy neural network modeling the temperature of distillation. Dynamic fuzzy neural network adopt dynamic learning algorithm, and characteristic of approximation. The simulation results show the effect and accuracy of Dynamic fuzzy neural network model ing method.

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33

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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34

Hou, Guo Qiang, Wei Jie Zhao, and Si Lan Li. "Research of Power Plant Boiler Control System Based on Compensatory Fuzzy Neural Network." Applied Mechanics and Materials 614 (September 2014): 203–6. http://dx.doi.org/10.4028/www.scientific.net/amm.614.203.

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Considering thermal power plant boiler’s nonlinear, large delay and time-varying, the paper proposes a compensatory fuzzy neural network control based on fuzzy control and neural network control. The compensatory fuzzy neural network is better than the PID controller and general fuzzy network controller in properties by using fuzzy inference and compensatory arithmetic. The paper makes a preliminary simulation using simulation tools of Matlab. And, the superiority of the compensatory fuzzy neural network control is proved by comparing the two kinds of simulation.

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35

Liang, Xifeng, Ming Peng, Jie Lu, and Chao Qin. "A Visual Servo Control Method for Tomato Cluster-Picking Manipulators Based on a T-S Fuzzy Neural Network." Transactions of the ASABE 64, no. 2 (2021): 529–43. http://dx.doi.org/10.13031/trans.13485.

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HighlightsA T-S fuzzy neural network was applied to the visual servo control system of a tomato picking manipulator.The T-S fuzzy neural network structure was designed, and collected data were used to train the neural network model.A visual servo control system for the picking manipulator based on the neural network was designed and tested.The T-S fuzzy neural network was superior to a BP neural network in visual servo control of the picking manipulator.Abstract. To reduce the computational load of image Jacobian matrix estimation and to avoid the appearance of singularity of a Jacobian matrix

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36

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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37

Januar, Deri, and Mutia Nur Estri. "FUZZY NEURAL NETWORK DENGAN NEURON FUZZYKWAN DAN CAI SERTA APLIKASINYA UNTUK EVALUASI KINERJA PERTAHANAN KLUB SEPAK BOLA." Jurnal Ilmiah Matematika dan Pendidikan Matematika 5, no. 1 (2013): 23. http://dx.doi.org/10.20884/1.jmp.2013.5.1.2913.

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In this paper, we discuss fuzzy neural network with neuron fuzzy Kwan dan Cai. The algorithm used in this fuzzy neural network is the feed forward algorithm. Futhermore, fuzzy neural network with neuron fuzzy Kwan and Cai can be applied to evaluate a defense performance of football clubs. In this application, we do calculation manually and using a program.

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38

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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39

Lee, K., Dong-Hoon Kwak, and Hyung Lee-Kwang. "Fuzzy Inference Neural Network for Fuzzy Model Tuning." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 26, no. 4 (1996): 637. http://dx.doi.org/10.1109/tsmcb.1996.517039.

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40

Hengjie, Song, Miao Chunyan, Shen Zhiqi, Miao Yuan, and Bu-Sung Lee. "A fuzzy neural network with fuzzy impact grades." Neurocomputing 72, no. 13-15 (2009): 3098–122. http://dx.doi.org/10.1016/j.neucom.2009.03.009.

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41

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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42

Buckley, James J., and Yoichi Hayashi. "Can fuzzy neural nets approximate continuous fuzzy functions?" Fuzzy Sets and Systems 61, no. 1 (1994): 43–51. http://dx.doi.org/10.1016/0165-0114(94)90283-6.

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43

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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44

Keon-Myung Lee, Dong-Hoon Kwak, and Hyung Lee-Kwang. "Fuzzy inference neural network for fuzzy model tuning." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 26, no. 4 (1996): 637–45. http://dx.doi.org/10.1109/3477.517027.

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45

Chin-Teng Lin and Ya-Ching Lu. "A neural fuzzy system with fuzzy supervised learning." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 26, no. 5 (1996): 744–63. http://dx.doi.org/10.1109/3477.537316.

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46

Hayashi, Yoichi, James J. Buckley, and Ernest Czogala. "Fuzzy neural network with fuzzy signals and weights." International Journal of Intelligent Systems 8, no. 4 (1993): 527–37. http://dx.doi.org/10.1002/int.4550080405.

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47

Chen, Zhijia, Yuanchang Zhu, Yanqiang Di, and Shaochong Feng. "Self-Adaptive Prediction of Cloud Resource Demands Using Ensemble Model and Subtractive-Fuzzy Clustering Based Fuzzy Neural Network." Computational Intelligence and Neuroscience 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/919805.

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In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning al

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48

Rabieyan, Reza, and Philipp Pohl. "Improving a fuzzy neural network for predicting storage usage and calculating customer value." Journal of Revenue and Pricing Management 19, no. 5 (2020): 292–301. http://dx.doi.org/10.1057/s41272-020-00253-3.

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Abstract Predicting the behavior of customers plays a crucial role in the quality of resource management and customer services. In this article, a fuzzy neural network model for predicting the customer storage usage is identified. The identified fuzzy neural network is improved and finally the result of the improved fuzzy neural network is compared with some other fuzzy neural network and other prediction methods.

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49

Chen, Xiang, and Xue Feng Zhou. "Research on Prediction of Excavation Deformation Based on Fuzzy Neural Network." Applied Mechanics and Materials 71-78 (July 2011): 3992–95. http://dx.doi.org/10.4028/www.scientific.net/amm.71-78.3992.

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Seeing that there are a lot of uncertain and fuzzy factors in the deformation control system of excavation, in this paper, the author combined the fuzzy theory with neural network technology, adopted the ordinary non-linear structure as the fuzzy neural network of neural network structure formed of neuron directly, and it is under the degree fuzzy number which inputs and export the information to import the corresponding network. Thus according to fuzzy neural network, displacement prediction model has been set up.

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50

Wang, Wei Ping, and Li Zhou. "Research on Intelligent Control Technology with Building Energy Control Model Based on Intelligent Control Algorithm." Advanced Materials Research 1014 (July 2014): 329–32. http://dx.doi.org/10.4028/www.scientific.net/amr.1014.329.

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For the current smart building energy control algorithms are still large energy loss, poor energy-saving effect and other issues, this paper presents a fuzzy neural network algorithm based on improved BP algorithm, the improved algorithm of BP neural network algorithm first reverse dissemination and weighting coefficients are adjusted to accelerate the convergence rate of the original algorithm, and then build the improved BP neural network algorithm for fuzzy neural network, and then to improve it fuzzy membership function parameters to improve the efficiency of fuzzy neural network learning.

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