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Artículos de revistas sobre el tema "Fuzzy neural networks"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 9 de junio de 2025

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1

Yang, Xin. "Quantum fuzzy neural network based on fuzzy number." Frontiers in Computing and Intelligent Systems 3, no. 2 (April 13, 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, the cross-fusion of "fuzzy number theory + quantum neural network" is proposed to study the quantum fuzzy neural network (FQNN) based on fuzzy number. The Gaussian fuzzy function is used to generate the corresponding fuzzy affiliation matrix to describe the uncertain information in the data. The fuzzy independent variables are trained through the FQNN model, and the model is output after changing the parameters of the quantum forward propagation layer. Simulation experiments show that the quantum fuzzy neural network model based on fuzzy number is more efficient and accurate in this study compared with the quantum neural network model.

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2

Md., Musa Khan. "Comparison of Selection Method of a Membership Function for Fuzzy Neural Networks." International Journal of Case Studies 6, no. 11 (November 30, 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 for determining the alternate approach of the fuzzy neural network.

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3

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

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4

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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5

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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6

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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7

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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8

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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9

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

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10

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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11

Rutkowska, Danuta, and Yoichi Hayashi. "Neuro-Fuzzy Systems Approaches." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 3 (June 20, 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, adaptive fuzzy systems, fuzzy inference neural networks, etc. Two different defuzzifiers, applied to fuzzy systems, are in focus of the paper. Center-of-sums method is an example of parametric defuzzification. Standard neural networks a defuzzifier presents nonparametric approach to defuzzification. For both cases learning algorithms of neuro-fuzzy systems are proposed. These algorithms take a form of recursions derived based on the momentum back-propagation method. Computer simulation demonstrates a comparison between performance of neuro-fuzzy systems with the parametric and nonparametric defuzzifier. Truck backer-upper control problem has been used to illustrate the systems performance. Conclusions concerning the simulation results are summarized. The paper pertains many references on neuro-fuzzy systems, especially selected publications of Czogala, whom it is dedicated.

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12

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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13

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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14

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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15

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

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16

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

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17

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 random disturbances, and the time delays are derived. Then, the upper bounds of the neutral terms, random disturbances, and time delays are estimated by solving the transcendental equations for multifactor perturbations, which ensures that the disturbed fuzzy cellular neural network can be stabilised again. Finally, the validity of the results is verified by numerical examples.</p>

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18

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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19

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

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20

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

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21

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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22

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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23

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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24

Gao, Fengyu, Jer-Guang Hsieh, Ying-Sheng Kuo, and Jyh-Horng Jeng. "Study on Resistant Hierarchical Fuzzy Neural Networks." Electronics 11, no. 4 (February 15, 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 level of the hierarchical fuzzy neural network. In order to enhance the resistance of the learning machines, we use the least trimmed squared error as the cost function. To test the resistance of learning machines to adverse effects of outliers, we add at the output node some noise from three different types of distributions, namely, normal, Laplace, and uniform distributions. Real-world datasets are used to compare the performances of the proposed resistant hierarchical fuzzy neural networks, resistant densely connected artificial neural networks, and densely connected artificial neural networks without noise.

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25

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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26

Melin, Patricia, Julio Cesar Monica, Daniela Sanchez, and Oscar Castillo. "Multiple Ensemble Neural Network Models with Fuzzy Response Aggregation for Predicting COVID-19 Time Series: The Case of Mexico." Healthcare 8, no. 2 (June 19, 2020): 181. http://dx.doi.org/10.3390/healthcare8020181.

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In this paper, a multiple ensemble neural network model with fuzzy response aggregation for the COVID-19 time series is presented. Ensemble neural networks are composed of a set of modules, which are used to produce several predictions under different conditions. The modules are simple neural networks. Fuzzy logic is then used to aggregate the responses of several predictor modules, in this way, improving the final prediction by combining the outputs of the modules in an intelligent way. Fuzzy logic handles the uncertainty in the process of making a final decision about the prediction. The complete model was tested for the case of predicting the COVID-19 time series in Mexico, at the level of the states and the whole country. The simulation results of the multiple ensemble neural network models with fuzzy response integration show very good predicted values in the validation data set. In fact, the prediction errors of the multiple ensemble neural networks are significantly lower than using traditional monolithic neural networks, in this way showing the advantages of the proposed approach.

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27

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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28

Pedrycz, Witold. "Logic - Oriented Fuzzy Neural Networks." International Journal of Hybrid Intelligent Systems 1, no. 1-2 (September 13, 2004): 3–11. http://dx.doi.org/10.3233/his-2004-11-203.

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29

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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30

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

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31

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

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32

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

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33

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

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34

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

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35

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

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36

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 (September 2009): 2553–66. http://dx.doi.org/10.1016/j.fss.2008.12.018.

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37

Chen, Xiaoxu, Linyuan Wang, and Zhiyu Huang. "Principal Component Analysis Based Dynamic Fuzzy Neural Network for Internal Corrosion Rate Prediction of Gas Pipelines." Mathematical Problems in Engineering 2020 (September 17, 2020): 1–9. http://dx.doi.org/10.1155/2020/3681032.

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Aiming at the characteristics of the nonlinear changes in the internal corrosion rate in gas pipelines, and artificial neural networks easily fall into a local optimum. This paper proposes a model that combines a principal component analysis (PCA) algorithm and a dynamic fuzzy neural network (D-FNN) to address the problems above. The principal component analysis algorithm is used for dimensional reduction and feature extraction, and a dynamic fuzzy neural network model is utilized to perform the prediction. The study implementing the PCA-D-FNN is further accomplished with the corrosion data from a real pipeline, and the results are compared among the artificial neural networks, fuzzy neural networks, and D-FNN models. The results verify the effectiveness of the model and algorithm for inner corrosion rate prediction.

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38

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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39

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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40

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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41

Muradova, Alevtina Aleksandrovna. "USING KOHONEN NEURAL NETWORKS AND FUZZY NEURAL NETWORKS IN INTELLIGENT ANALYSIS OF IoT SENSOR INFORMATION." SCHOLAR 3, no. 1 (February 1, 2025): 4–11. https://doi.org/10.5281/zenodo.14784357.

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<em>The article presents methods for using Kohonen neural networks and fuzzy neural networks in intelligent analysis of information from IoT sensors. A detailed data analysis process based on a neural network is shown. The types of intelligent data analysis based on neural networks are considered. The advantages and disadvantages of popular neural networks in data mining are also given.</em>

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42

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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43

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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44

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

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45

Sotirov, Sotir, Maciej Krawczak, Diana Petkova, and Krassimir Atanassov. "Intuitionistic fuzzy neural network with filtering functions. An index matrix interpretation." Notes on Intuitionistic Fuzzy Sets 29, no. 2 (August 2023): 231–38. http://dx.doi.org/10.7546/nifs.2023.29.2.231-238.

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Biological neurons and their connection in neural networks have motivated the creation of the architecture of artificial neural networks. In the previously considered cases, the description of the neural networks and their connections are described with standard matrices where the values for the weighting coefficients and biases are placed. By recalculating them, the artificial neural network is trained. The paper presents an approach for describing multilayer neural networks with Intuitionistic Fuzzy Index Matrix (IFIM). The neural network input was described in IFIM form, then the weight coefficients of the connections between the nodes of the input vector, and then activation functions of the neurons. The use of IFIM extends the understanding and description as well as the structure and use of multilayer neural networks.

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46

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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47

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 (March 2001): 351–58. http://dx.doi.org/10.1016/s0165-0114(98)00461-8.

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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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49

Mazin Hashim Suhhiem and Basim Nasih Abood. "Fully Fuzzy Neural Network For Solving Fuzzy Differential Equations." Journal of Wasit for Science and Medicine 11, no. 1 (August 15, 2023): 32–45. http://dx.doi.org/10.31185/jwsm.438.

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In this work we have introduced a new method for solving fuzzy differential equations .This method "based on the" fully fuzzy "neural network" to find "the numerical solution of" the "first order fuzzy differential equations" ."The fuzzy trial solution of" the "fuzzy initial value problem is written as a sum of two parts". The first part satisfies the fuzzy condition, it contains no fuzzy adjustable parameters. The second part involves fully fuzzy feed-forward neural networks containing fuzzy adjustable parameters. "Under some conditions the proposed method provides numerical solutions with high accuracy".

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50

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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