Статті в журналах: "Neural fuzzy network"

1

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

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2

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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3

Yerokhin, A. L., and O. V. Zolotukhin. "Fuzzy probabilistic neural network in document classification tasks." Information extraction and processing 2018, no. 46 (December 27, 2018): 68–71. http://dx.doi.org/10.15407/vidbir2018.46.068.

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4

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 intelligent control system.

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5

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

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6

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

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7

Li, Ye, Xiao Liu, Zhenliang Yang, Chao Zhang, Mingchun Song, Zhaolu Zhang, Shiyong Li, and Weiqiang Zhang. "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, Eastern China, as a study case, this paper uses the method of combining artificial neural network and fuzzy logic to study geologically complicated fault structure prediction model. This paper expounds on the research status and significance of geologically complicated fault structure prediction model, elaborates the development background, current status, and future challenges of artificial neural networks and fuzzy logic, introduces the method and principle of fuzzy neural network structure and fuzzy logic analysis algorithm, conducts prediction model design and implementation based on fuzzy neural network, proposes the learning algorithm of fuzzy neural network, analyzes the programming realization of fuzzy neural network, constructs complicated fault structure prediction model based on the artificial neural network and fuzzy logic, performs the fuzzy logic system selection of complicated fault structure prediction model, carries out the artificial neural network structure design of complicated fault structure prediction model, compares the prediction effects of the geologically complicated fault structure model based on artificial neural networks and fuzzy logic, and finally discusses the system design and optimization of the prediction model for geologically complicated fault structures. The study results show that the fuzzy neural network fully integrates the advantages of artificial neural network and fuzzy logic system; based on the clear physical background of fuzzy logic system, it effectively integrates powerful knowledge expression ability and fuzzy reasoning ability into the network knowledge structure of neural network, which greatly improves the prediction accuracy of geologically complicated fault structure.

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8

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 performance and it can meet the needs of heating furnace during industrial production.

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9

Thakur, Amey. "Neuro-Fuzzy: Artificial Neural Networks & Fuzzy Logic." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 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 and fuzzy logic systems are parameterized nonlinear computing methods for numerical data processing (signals, images, stimuli). These algorithms can be integrated into dedicated hardware or implemented on a general-purpose computer. The network system acquires knowledge through a learning process. Internal parameters are used to store the learned information (weights). Keywords: Artificial Neural Networks (ANNs), Neural Networks (NNs), Fuzzy Logic (FL), Neuro-Fuzzy, Probability Reasoning, Soft Computing, Fuzzification, Defuzzification, Fuzzy Inference Systems, Membership Function.

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10

Teja, G. Ravi, and M. R. Narasinga Rao. "Image Retrieval System using Fuzzy-Softmax MLP Neural Network." Indian Journal of Applied Research 3, no. 6 (October 1, 2011): 169–74. http://dx.doi.org/10.15373/2249555x/june2013/57.

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11

Godoy Simões, Marcelo, and Bimal K. Bose. "Fuzzy neural network based estimation of power electronic waveforms." Eletrônica de Potência 1, no. 1 (June 1, 1996): 64–70. http://dx.doi.org/10.18618/rep.1996.1.064070.

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12

Wutsqa, Dhoriva Urwatul, and Anisa Nurjanah. "Breast Cancer Classification Using Fuzzy Elman Recurrent Neural Network." Journal of Advanced Research in Dynamical and Control Systems 11, no. 11-SPECIAL ISSUE (November 20, 2019): 946–53. http://dx.doi.org/10.5373/jardcs/v11sp11/20193119.

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13

Bodyansky, E. V., and Т. Е. Antonenko. "Deep neo-fuzzy neural network and its learning." Bionics of Intelligence 1, no. 92 (June 2, 2019): 3–8. http://dx.doi.org/10.30837/bi.2019.1(92).01.

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Optimizing the learning speedof deep neural networks is an extremely important issue. Modern approaches focus on the use of neural networksbased on the Rosenblatt perceptron. But the results obtained are not satisfactory for industrial and scientific needs inthe context of the speed of learning neural networks. Also, this approach stumbles upon the problems of a vanishingand exploding gradient. To solve the problem, the paper proposed using a neo-fuzzy neuron, whose properties arebased on the F-transform. The article discusses the use of neo-fuzzy neuron as the main component of the neuralnetwork. The architecture of a deep neo-fuzzy neural network is shown, as well as a backpropagation algorithmfor this architecture with a triangular membership function for neo-fuzzy neuron. The main advantages of usingneo-fuzzy neuron as the main component of the neural network are given. The article describes the properties of aneo-fuzzy neuron that addresses the issues of improving speed and vanishing or exploding gradient. The proposedneo-fuzzy deep neural network architecture is compared with standard deep networks based on the Rosenblattperceptron.

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14

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 (June 29, 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. The recognition rate (accuracy) with the data features is found to be 98.06%. Fuzzy classifier shows higher significant value of P=0.002 < P=0.005 than the neural networks in recognition of characters. Conclusion: The independent tests for this study shows a higher accuracy level of alphabetical character recognition for Fuzzy classifier when compared with neural networks. Henceforth, the fuzzy classifier shows higher significant than the neural networks in recognition of characters.

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15

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 (June 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 in the condition space while P(x) is a polynomial standing in the conclusion part of the rule. The conclusion part of the rules, especially the regression polynomial uses several types of high-order polynomials such as constant, linear, quadratic, and modified quadratic. As the premise part of the rules, both triangular and Gaussian-like membership functions are considered. Each PN of the network realizes a polynomial type of partial description (PD) of the mapping between input and out variables. HFPNN is a flexible neural architecture whose structure is based on the Group Method of Data Handling (GMDH) and developed through learning. In particular, the number of layers of the PNN is not fixed in advance but is generated in a dynamic way. The experimental part of the study involves two representative numerical examples such as chaotic time series and Box-Jenkins gas furnace data.

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16

Shapoval, Nataliia. "TSK Fuzzy Neural Network Use for COVID-19 Classification." Electronics and Control Systems 1, no. 71 (June 27, 2022): 50–54. http://dx.doi.org/10.18372/1990-5548.71.16825.

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It is considered t the Takagi-Sugeno-Kang fuzzy neural network and its modern variations. The use of regularization, random exclusion of rules from the rule base allows solving the problem of excessive similarity of rules in the rule base. The use of batch normalization to increase the generalizing properties of the network allows to increase the accuracy of the model, while maintaining the possibility of interpreting the results, which is characteristic of fuzzy neural networks. It is proposed to use an ensemble of fuzzy neural networks to increase the generalizing capabilities of the network. Studies of the Takagi-Sugeno-Kang fuzzy neural network for the task of diagnosing the coronavirus disease show that the proposed model works well and allows to improve the result.

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17

Zhang, Yitong, Hideya Takahashi, Kazuo Shigeta, and Eiji Shimizu. "Adaptive Fuzzy Classification Neural Network." IEEJ Transactions on Electronics, Information and Systems 115, no. 4 (1995): 589–96. http://dx.doi.org/10.1541/ieejeiss1987.115.4_589.

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18

Gücüyener, İsmet. "Fuzzy Neural-Network-Based Controller." Solid State Phenomena 220-221 (January 2015): 407–12. http://dx.doi.org/10.4028/www.scientific.net/ssp.220-221.407.

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Using a controller is necessary for any automation system. The controller must be cheap, reliable, user friendly and not cause any problems for inputs and outputs. Classical control systems like proportional integral derivative (PID) put adequate results of linear systems and continuous-time. In fact, real control systems are time-variant, with non-linearity and poorly calculated dynamic variables. For this reason, conventional control systems need an expert person to adjust controller parameters in general. Sometimes an operator is required to solve control problems. Human control is not completely reliable. Also, it does not include any electronic communication. In modern factories, every point must be monitored and electronically controlled from remote points when necessary. In this study, including every electronic communication channel, a simplified handling, low-cost, reliable, Fuzzy Neural Network Controller (FNNC) is designed.

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19

Ebadzadeh, Mohammad Mehdi, and Armin Salimi-Badr. "CFNN: Correlated fuzzy neural network." Neurocomputing 148 (January 2015): 430–44. http://dx.doi.org/10.1016/j.neucom.2014.07.021.

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20

Iyatomi, Hitoshi, and Masafumi Hagiwara. "Adaptive fuzzy inference neural network." Pattern Recognition 37, no. 10 (October 2004): 2049–57. http://dx.doi.org/10.1016/j.patcog.2004.04.003.

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21

Dutra, Rogerio Garcia, and Moacyr Martucci. "Adaptive Fuzzy Neural Tree Network." IEEE Latin America Transactions 6, no. 5 (September 2008): 453–60. http://dx.doi.org/10.1109/tla.2008.4839115.

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22

Zhao, Jing, Zhao Lin Han, and Yuan Yuan Fang. "Fuzzy Neural Network Hybrid Learning Control on AUV." Advanced Materials Research 468-471 (February 2012): 1732–35. http://dx.doi.org/10.4028/www.scientific.net/amr.468-471.1732.

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A novel controller based on the fuzzy B-spline neural network is presented, which combines the advantages of qualitative defining capability of fuzzy logic, quantitative learning ability of neural networks and excellent local controlling ability of B-spline basis functions, which are being used as fuzzy functions. A hybrid learning algorithm of the controller is proposed as well. The results show that it is feasible to design the fuzzy neural network control of autonomous underwater vehicle by the hybrid learning algorithm.

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23

Tang, Lu Xin, Bin Bin, and Kun Han. "The FNN Quilting Process Deformation Prediction Model." Applied Mechanics and Materials 34-35 (October 2010): 306–10. http://dx.doi.org/10.4028/www.scientific.net/amm.34-35.306.

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It is easy to have deformation that non-rigid materials is on high-speed processing, so this paper introduces the fuzzy neural networks combine with computer vision measurement technology to control this process. Based on the traditional PID control, increasing a fuzzy neural network predictor for pre-processing of trajectory compensation. Established a fuzzy neural network deformation prediction model of the single needle quilting, and simulated. Experimental and simulation results show that: error compensation which based on fuzzy neural network, have a good real-time, allow fast and accurate automated processing of quilting.

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24

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, fuzzy clustering and grey relational clustering methods are proposed to substitute K-means clustering, RBF center is determined by the results of fuzzy clustering or grey relational clustering, and some researches of RBF neural networks modeling accuracy are done. Practical modeling cases demonstrate that the modeling accuracy of fuzzy clustering RBF neural networks and grey relational clustering RBF neural networks are significantly better than K-means clustering RBF neural networks, applying of fuzzy clustering or grey relational clustering to determine the basis function center of RBF neural networks hidden layer node is feasible and effective.

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25

Oliver Muncharaz, J. "Hybrid fuzzy neural network versus backpropagation neural network: An application to predict the Ibex-35 index stock." Finance, Markets and Valuation 6, no. 1 (2020): 85–98. http://dx.doi.org/10.46503/alep9985.

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The use of neural networks has been extended in all areas of knowledge due to the good results being obtained in the resolution of the different problems posed. The prediction of prices in general, and stock market prices in particular, represents one of the main objectives of the use of neural networks in finance. This paper presents the analysis of the efficiency of the hybrid fuzzy neural network against a backpropagation type neural network in the price prediction of the Spanish stock exchange index (IBEX-35). The paper is divided into two parts. In the first part, the main characteristics of neural networks such as hybrid fuzzy and backpropagation, their structures and learning rules are presented. In the second part, the prediction of the IBEX-35 stock exchange index with these networks is analyzed, measuring the efficiency of both as a function of the prediction errors committed. For this purpose, both networks have been constructed with the same inputs and for the same sample period. The results obtained suggest that the Hybrid fuzzy neuronal network is much more efficient than the widespread backpropagation neuronal network for the sample analysed.

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26

Virgil Negoita, Constantin. "Neural Networks as Fuzzy Systems." Kybernetes 23, no. 3 (April 1, 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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27

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 (August 1996): 637. http://dx.doi.org/10.1109/tsmcb.1996.517039.

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28

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 (August 2009): 3098–122. http://dx.doi.org/10.1016/j.neucom.2009.03.009.

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29

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 (August 1996): 637–45. http://dx.doi.org/10.1109/3477.517027.

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30

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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31

Kuznetsov, Vladlen, Sergey Dyadun, and Valentin Esilevsky. "The control to aggregates of pumping stations using a regulator based on a neural network with fuzzy logic." E3S Web of Conferences 102 (2019): 03007. http://dx.doi.org/10.1051/e3sconf/201910203007.

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A pumping station control system is considered using a controller based on a fuzzy logic neural network. The simulation of the classical and fuzzy regulators. The possibility of the implementation of the controller in the form of an adaptive multilayer neural network is shown. The use of the theory of fuzzy sets in combination with the theory of neural networks to create a fuzzy-neural regulator to control pumping units provides a promising approach. Simulation modeling and real operation have shown that fuzzy-logic regulators have a number of advantages over classical regulators, which allow the use of form and limitations. Using the neural network model allows you to add the properties of adaptability and learning. The fuzzy-neural controller for controlling pumping units is promising in terms of efficiency and safety by controlling pumping stations.

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32

Shi, Jian Jun, La Wu Zhou, Ke Wen Kong, and Yi Wang. "Fuzzy Neural Network Based Coal-Rock Interface Recognition." Applied Mechanics and Materials 44-47 (December 2010): 1402–6. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.1402.

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. In the coal-rock interface recognition (CIR) technology, signal process and recognition are the key parts. A method for CIR based on BP neural networks and fuzzy technique was proposed in this paper. By using the trail-and-error, the hidden layer dimension of the network was decided. Also the network training and weight modification were studied. In order to get a higher identification ratio, fuzzy neural networks (FNN) based data fusion was studied. For CIR, the structure and algorithm of FNN were determined. The results indicated that the test data can be used to train and simulate with the neural network and FNN. And the proposed method can be used in CIR with a higher recognition ratio.

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33

Guan, Li Ming, Jia Xing Tian, Chen Lu, and We Zhang. "Study on Temperature Control System of Film Laminating Machine." Applied Mechanics and Materials 397-400 (September 2013): 1263–70. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.1263.

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The recursive compensatory fuzzy neural network model was established against the characteristics of temperature control for film laminating machine. The neural network can be used to construct the fuzzy system, and the self-adaptive and self-learning capability of neural networks was used to automatically adjust fuzzy system parameters, BP network could be learned and trained by the gradient descent algorithm. Based on the test data for the study and testing of network, system error is less than the national standard error requirements, the results proved the effectiveness and feasibility of the algorithm.

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34

D’ALCHÉ-BUC, FLORENCE, VINCENT ANDRÈS, and JEAN-PIERRE NADAL. "RULE EXTRACTION WITH FUZZY NEURAL NETWORK." International Journal of Neural Systems 05, no. 01 (March 1994): 1–11. http://dx.doi.org/10.1142/s0129065794000025.

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This paper deals with the learning of understandable decision rules with connectionist systems. Our approach consists of extracting fuzzy control rules with a new fuzzy neural network. Whereas many other works on this area propose to use combinations of nonlinear neurons to approximate fuzzy operations, we use a fuzzy neuron that computes max-min operations. Thus, this neuron can be interpreted as a possibility estimator, just as sigma-pi neurons can support a probabilistic interpretation. Within this context, possibilistic inferences can be drawn through the multi-layered network, using a distributed representation of the information. A new learning procedure has been developed in order that each part of the network can be learnt sequentially, while other parts are frozen. Each step of the procedure is based on the same kind of learning scheme: the backpropagation of a well-chosen cost function with appropriate derivatives of max-min function. An appealing result of the learning phase is the ability of the network to automatically reduce the number of the condition-parts of the rules, if needed. The network has been successfully tested on the learning of a control rule base for an inverted pendulum.

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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 in the visual servo control of a picking manipulator, a T-S fuzzy neural network algorithm is proposed to replace the image Jacobian matrix. This better fits the hand-eye relationship by combining the knowledge structure of fuzzy reasoning with the self-learning ability of a neural network. The T-S fuzzy neural network was trained and tested by collecting the variation data of image features and joint angles; after training, the T-S fuzzy neural network was used to predict the joint angles of the picking manipulator. Simulation results show that the square sum of training errors and testing errors were 0.017 and 0.032, respectively, after training the T-S fuzzy neural network. A T-S fuzzy neural network controller was applied to the visual servo system of the picking robot, and the test results show that the average difference between the end-effector and the ultimate target location of the visual servo system based on the T-S fuzzy neural network controller was 0.0037 m, which was 79.44% less than that of the visual servo system based on a BP neural network. The final average error of image features was between 0.52 and 3.25 pixels, which was 74.932% less than that of the visual servo system based on the BP neural network. Keywords: Picking manipulator, Tomato clusters, T-S fuzzy neural network, Visual servoing.

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36

Lippe, Wolfram-M., Steffen Niendieck, and Andreas Tenhagen. "On the Optimization of Fuzzy-Controllers by Neural Networks." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 3 (June 20, 1999): 158–63. http://dx.doi.org/10.20965/jaciii.1999.p0158.

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Methods are known for combining fuzzy-controllers with neural networks. One of the reasons of these combinations is to work around the fuzzy controllers’ disadvantage of not being adaptive. It is helpful to represent a given fuzzy controller by a neural network and to have rules adapted by a special learning algorithm. Some of these methods are applied in the NEFCONmode or the model of Lin and Lee. Unfortunately, none adapts all fuzzy-controller components. We suggest a new model enabling the user to represent a given fuzzy controller by a neural network and adapt its components as desired.

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37

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 (August 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. Next, a training algorithm is derived from the cost function in a similar manner as the back-propagation algorithm. Last, we examine the ability of our approach by computer simulations on numerical examples. Simulation results show that the proposed algorithm is able to reduce the outlier effects.

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38

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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39

Pakhomova, V., and A. Vydish. "Study of the combined variant of determination of attacks using neural network technologies." System technologies 3, no. 140 (April 8, 2022): 79–86. http://dx.doi.org/10.34185/1562-9945-3-140-2022-08.

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The modern world is impossible to imagine without computer networks: both local and global; therefore, the issue of network security is becoming increasingly topical. Currently, methods of detecting attacks can be strengthened by using neural networks, which confirms the relevance of the topic. The aim of the study is a comparative analysis of the quality parameters of network attacks using a combined variant consisting of different neural networks. As research methods used: neural network; multilayer perceptron; Kohonen's self-organizing map. The software implementation of the Kohonen self-organizing map is carried out in Python with a wide range of modern standard tools, creation of a multilayer perceptron and a fuzzy network - using Neural Network Toolbox packages, and Fuzzy Logic Toolbox system MatLAB. On the created neural networks separately and on their combined variant researches of parameters of quality of definition of network attacks are carried out. It was determined that the error of the first kind was 11%, 4%, 10% and 0%, the error of the second kind - 7%, 6%, 9% and 6% on the fuzzy network, multilayer perceptron, self-organizing Kohonen map and their combined version, respectively, which proves the feasibility of using the combined option.

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40

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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41

Wilamowski, B. M., J. Binfet, and M. O. Kaynak. "VLSI Implementation of Neural Networks." International Journal of Neural Systems 10, no. 03 (June 2000): 191–97. http://dx.doi.org/10.1142/s012906570000017x.

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Currently, fuzzy controllers are the most popular choice for hardware implementation of complex control surfaces because they are easy to design. Neural controllers are more complex and hard to train, but provide an outstanding control surface with much less error than that of a fuzzy controller. There are also some problems that have to be solved before the networks can be implemented on VLSI chips. First, an approximation function needs to be developed because CMOS neural networks have an activation function different than any function used in neural network software. Next, this function has to be used to train the network. Finally, the last problem for VLSI designers is the quantization effect caused by discrete values of the channel length (L) and width (W) of MOS transistor geometries. Two neural networks were designed in 1.5 μm technology. Using adequate approximation functions solved the problem of activation function. With this approach, trained networks were characterized by very small errors. Unfortunately, when the weights were quantized, errors were increased by an order of magnitude. However, even though the errors were enlarged, the results obtained from neural network hardware implementations were superior to the results obtained with fuzzy system approach.

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42

Tian, Miao. "Diagnose Expert System of Engine Based on Fuzzy Neural Network." Advanced Materials Research 588-589 (November 2012): 1472–75. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.1472.

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Engine has a high chance of failure, it usually accounts for about 40% of vehicle failures. Study expert system of engine fault diagnosises that it can locate fault timely and accurately, and enhance efficiency. However, the traditional expert system has shortcomings so as inefficient inference and poor self-learning capability. The fuzzy logic and traditional neural networks are combined to form fuzzy neural networks, they are established a model of fuzzy neural network (FNN) of fault diagnosis, and that the model is applied to engine fault diagnosis, complementary advantages, to effectively enhance efficiency of inference and self-learning ability, its performance is higher than the traditional BP network.

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43

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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44

LING, S. H., F. H. F. LEUNG, and H. K. LAM. "AN IMPROVED GENETIC ALGORITHM BASED FUZZY-TUNED NEURAL NETWORK." International Journal of Neural Systems 15, no. 06 (December 2005): 457–74. http://dx.doi.org/10.1142/s0129065705000438.

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This paper presents a fuzzy-tuned neural network, which is trained by an improved genetic algorithm (GA). The fuzzy-tuned neural network consists of a neural-fuzzy network and a modified neural network. In the modified neural network, a neuron model with two activation functions is used so that the degree of freedom of the network function can be increased. The neural-fuzzy network governs some of the parameters of the neuron model. It will be shown that the performance of the proposed fuzzy-tuned neural network is better than that of the traditional neural network with a similar number of parameters. An improved GA is proposed to train the parameters of the proposed network. Sets of improved genetic operations are presented. The performance of the improved GA will be shown to be better than that of the traditional GA. Some application examples are given to illustrate the merits of the proposed neural network and the improved GA.

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45

Taleb, A., and A. Benyettou. "Arabic Vowels Fuzzy Neural Network Recognition." Journal of Applied Sciences 10, no. 10 (May 1, 2010): 848–51. http://dx.doi.org/10.3923/jas.2010.848.851.

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46

IMASAKI, Naoki, Jun-ichi KIJI, and Tsunekazu ENDO. "A Fuzzy Rule Structured Neural Network." Journal of Japan Society for Fuzzy Theory and Systems 4, no. 5 (1992): 985–95. http://dx.doi.org/10.3156/jfuzzy.4.5_985.

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47

SHIRAI, Yoshiaki. "Neural network and fuzzy in robotics." Journal of the Robotics Society of Japan 9, no. 2 (1991): 204–8. http://dx.doi.org/10.7210/jrsj.9.204.

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48

Kulkarni, U. V., T. R. Sontakke, and A. B. Kulkarni. "Fuzzy hyperline segment clustering neural network." Electronics Letters 37, no. 5 (2001): 301. http://dx.doi.org/10.1049/el:20010198.

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49

Chak, Chu Kwong, and Gang Feng. "A New Fuzzy Neural Network System." Journal of Intelligent and Fuzzy Systems 3, no. 2 (1995): 131–44. http://dx.doi.org/10.3233/ifs-1995-3203.

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50

Uebele, V., S. Abe, and Ming-Shong Lan. "A neural-network-based fuzzy classifier." IEEE Transactions on Systems, Man, and Cybernetics 25, no. 2 (1995): 353–61. http://dx.doi.org/10.1109/21.364829.

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