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Journal articles on the topic 'Neuro-Fuzzy Approach'

Author: Grafiati

Published: 20 April 2024

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1

Simiński, Krzysztof. "Neuro-rough-fuzzy approach for regression modelling from missing data." International Journal of Applied Mathematics and Computer Science 22, no. 2 (June 1, 2012): 461–76. http://dx.doi.org/10.2478/v10006-012-0035-4.

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Neuro-rough-fuzzy approach for regression modelling from missing dataReal life data sets often suffer from missing data. The neuro-rough-fuzzy systems proposed hitherto often cannot handle such situations. The paper presents a neuro-fuzzy system for data sets with missing values. The proposed solution is a complete neuro-fuzzy system. The system creates a rough fuzzy model from presented data (both full and with missing values) and is able to elaborate the answer for full and missing data examples. The paper also describes the dedicated clustering algorithm. The paper is accompanied by results of numerical experiments.

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2

Han, Man-Wook, and Peter Kopacek. "Neuro-Fuzzy Approach in Service Robotics." IFAC Proceedings Volumes 29, no. 1 (June 1996): 760–65. http://dx.doi.org/10.1016/s1474-6670(17)57753-8.

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3

Ray, Kumar S., and Jayati Ghoshal. "Neuro Fuzzy Approach to Pattern Recognition." Neural Networks 10, no. 1 (January 1997): 161–82. http://dx.doi.org/10.1016/s0893-6080(96)00056-1.

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4

Biswas, Saroj, Monali Bordoloi, and Biswajit Purkayastha. "Review on Feature Selection and Classification using Neuro-Fuzzy Approaches." International Journal of Applied Evolutionary Computation 7, no. 4 (October 2016): 28–44. http://dx.doi.org/10.4018/ijaec.2016100102.

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This research article attempts to provide a recent survey on neuro-fuzzy approaches for feature selection and classification. Feature selection acts as a catalyst in reducing computation time and dimensionality, enhancing prediction performance or accuracy and curtailing irrelevant or redundant data. The neuro-fuzzy approach is used for feature selection and for providing some insight to the user about the symbolic knowledge embedded within the network. The neuro–fuzzy approach combines the merits of neural network and fuzzy logic to solve many complex machine learning problems. The objective of this article is to provide a generic introduction and a recent survey to neuro-fuzzy approaches for feature selection and classification in a wide area of machine learning problems. Some of the existing neuro-fuzzy models are also applied on standard datasets to demonstrate the applicability of neuro-fuzzy approaches.

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5

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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6

Amirkhani, Abdollah, Hosna Nasiriyan-Rad, and Elpiniki I. Papageorgiou. "A Novel Fuzzy Inference Approach: Neuro-fuzzy Cognitive Map." International Journal of Fuzzy Systems 22, no. 3 (December 23, 2019): 859–72. http://dx.doi.org/10.1007/s40815-019-00762-3.

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7

Nowicki, Robert. "On classification with missing data using rough-neuro-fuzzy systems." International Journal of Applied Mathematics and Computer Science 20, no. 1 (March 1, 2010): 55–67. http://dx.doi.org/10.2478/v10006-010-0004-8.

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On classification with missing data using rough-neuro-fuzzy systemsThe paper presents a new approach to fuzzy classification in the case of missing data. Rough-fuzzy sets are incorporated into logical type neuro-fuzzy structures and a rough-neuro-fuzzy classifier is derived. Theorems which allow determining the structure of the rough-neuro-fuzzy classifier are given. Several experiments illustrating the performance of the roughneuro-fuzzy classifier working in the case of missing features are described.

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8

Sadeghi-Niaraki, Abolghasem, Ozgur Kisi, and Soo-Mi Choi. "Spatial modeling of long-term air temperatures for sustainability: evolutionary fuzzy approach and neuro-fuzzy methods." PeerJ 8 (August 14, 2020): e8882. http://dx.doi.org/10.7717/peerj.8882.

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This paper investigates the capabilities of the evolutionary fuzzy genetic (FG) approach and compares it with three neuro-fuzzy methods—neuro-fuzzy with grid partitioning (ANFIS-GP), neuro-fuzzy with subtractive clustering (ANFIS-SC), and neuro-fuzzy with fuzzy c-means clustering (ANFIS-FCM)—in terms of modeling long-term air temperatures for sustainability based on geographical information. In this regard, to estimate long-term air temperatures for a 40-year (1970–2011) period, the models were developed using data for the month of the year, latitude, longitude, and altitude obtained from 71 stations in Turkey. The models were evaluated with respect to mean absolute error (MAE), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and the determination coefficient (R2). All data were divided into three parts and every model was tested on each. The FG approach outperformed the other models, enhancing the MAE, RMSE, NSE, and R2 of the ANFIS-GP model, which yielded the highest accuracy among the neuro-fuzzy models by 20%, 30%, and 4%, respectively. A geographical information system was used to obtain temperature maps using estimates of the optimal models, and the results of the model were assessed using it.

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9

Srinivasan, Santhoshkumar, and Dhinesh Babu L.D. "A Neuro-Fuzzy Approach to Detect Rumors in Online Social Networks." International Journal of Web Services Research 17, no. 1 (January 2020): 64–82. http://dx.doi.org/10.4018/ijwsr.2020010104.

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Along with true information, rumors spread in online social networks (OSN) on an unprecedented scale. In recent days, rumor identification gains more interest among the researchers. Finding rumors also poses other critical challenges like noisy and imprecise input data, data sparsity, and unclear interpretations of the output. To address these issues, we propose a neuro-fuzzy classification approach called the neuro-fuzzy rumor detector (NFRD) to automatically identify the rumors in OSNs. NFRD quickly transforms the input to fuzzy rules which classify the rumor. Neural networks handle larger input data. Fuzzy systems are better in handling uncertainty and imprecision in input data by producing fuzzy rules that effectively eliminate the unclear inputs. NFRD also considers the semantic aspects of information to ensure better classification. The neuro-fuzzy approach addresses the most common problems such as uncertainty elimination, noise reduction, and quicker generalization. Experimental results show the proposed approach performs well against state-of-the-art rumor detecting techniques.

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10

VAIRAPPAN, CATHERINE, SHANGCE GAO, ZHENG TANG, and HIROKI TAMURA. "ANNEALED CHAOTIC LEARNING FOR TIME SERIES PREDICTION IN IMPROVED NEURO-FUZZY NETWORK WITH FEEDBACKS." International Journal of Computational Intelligence and Applications 08, no. 04 (December 2009): 429–44. http://dx.doi.org/10.1142/s1469026809002680.

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A new version of neuro-fuzzy system of feedbacks with chaotic dynamics is proposed in this work. Unlike the conventional neuro-fuzzy, improved neuro-fuzzy system with feedbacks is better able to handle temporal data series. By introducing chaotic dynamics into the feedback neuro-fuzzy system, the system has richer and more flexible dynamics to search for near-optimal solutions. In the experimental results, performance and effectiveness of the presented approach are evaluated by using benchmark data series. Comparison with other existing methods shows the proposed method for the neuro-fuzzy feedback is able to predict the time series accurately.

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11

ICHIHASHI, Hidetomo. "Tuning Fuzzy Rules by Neuro-Like Approach." Journal of Japan Society for Fuzzy Theory and Systems 5, no. 2 (1993): 191–203. http://dx.doi.org/10.3156/jfuzzy.5.2_191.

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12

Chandra, Vishal, and Savita Shiwani. "Fuzzy Neuro Approach for Active Queue Management." International Journal of Computer Applications 116, no. 22 (April 22, 2015): 34–38. http://dx.doi.org/10.5120/20470-2607.

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13

Chaw Seng, Woo, and Mahsa Chitsaz. "Handgrip Strength Evaluation Using Neuro Fuzzy Approach." Malaysian Journal of Computer Science 23, no. 3 (December 1, 2010): 166–76. http://dx.doi.org/10.22452/mjcs.vol23no3.3.

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14

Azhari, Muhammad, and Yogan Jaya Kumar. "Improving text summarization using neuro-fuzzy approach." Journal of Information and Telecommunication 1, no. 4 (August 22, 2017): 367–79. http://dx.doi.org/10.1080/24751839.2017.1364040.

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15

Lee, K. C., and P. Gardner. "Neuro-fuzzy approach to adaptive digital predistortion." Electronics Letters 40, no. 3 (2004): 185. http://dx.doi.org/10.1049/el:20040154.

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16

Muda @ Ismail, Muhammad Zaiyad. "Adaptive Neuro-fuzzy approach in friction identification." IOP Conference Series: Materials Science and Engineering 131 (May 2016): 012015. http://dx.doi.org/10.1088/1757-899x/131/1/012015.

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17

Pal, S. K., R. K. De, and J. Basak. "Unsupervised feature evaluation: a neuro-fuzzy approach." IEEE Transactions on Neural Networks 11, no. 2 (March 2000): 366–76. http://dx.doi.org/10.1109/72.839007.

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18

Frattale Mascioli, F. M., and G. Martinelli. "A constructive approach to neuro-fuzzy networks." Signal Processing 64, no. 3 (February 1998): 347–58. http://dx.doi.org/10.1016/s0165-1684(97)00200-4.

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19

De, Rajat K., Jayanta Basak, and Sankar K. Pal. "Unsupervised feature extraction using neuro-fuzzy approach." Fuzzy Sets and Systems 126, no. 3 (March 2002): 277–91. http://dx.doi.org/10.1016/s0165-0114(01)00070-7.

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20

Ghosh, Ashish, B. Uma Shankar, and Saroj K. Meher. "A novel approach to neuro-fuzzy classification." Neural Networks 22, no. 1 (January 2009): 100–109. http://dx.doi.org/10.1016/j.neunet.2008.09.011.

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21

Abu-al-nadi, D., and D. Popovic. "Texture identification using adaptive neuro-fuzzy approach." IFAC Proceedings Volumes 32, no. 2 (July 1999): 3874–78. http://dx.doi.org/10.1016/s1474-6670(17)56661-6.

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22

Caponetti, Laura, Ciro Castiello, and Przemysław Górecki. "Document page segmentation using neuro-fuzzy approach." Applied Soft Computing 8, no. 1 (January 2008): 118–26. http://dx.doi.org/10.1016/j.asoc.2006.11.008.

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23

Ilic, Milos, Srdjan Jovic, Petar Spalevic, and Igor Vujicic. "Water cycle estimation by neuro-fuzzy approach." Computers and Electronics in Agriculture 135 (April 2017): 1–3. http://dx.doi.org/10.1016/j.compag.2017.01.025.

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24

Chen, Mu-Song. "Neuro-fuzzy approach for online message scheduling." Engineering Applications of Artificial Intelligence 38 (February 2015): 59–69. http://dx.doi.org/10.1016/j.engappai.2014.10.002.

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25

Jović, Srđan, Obrad Aničić, Mladen Marsenić, and Bogdan Nedić. "Solar radiation analyzing by neuro-fuzzy approach." Energy and Buildings 129 (October 2016): 261–63. http://dx.doi.org/10.1016/j.enbuild.2016.08.020.

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26

Blahová, Lenka, Ján Dvoran, and Jana Kmeťová. "Neuro-fuzzy control design of processes in chemical technologies." Archives of Control Sciences 22, no. 2 (January 1, 2012): 233–50. http://dx.doi.org/10.2478/v10170-011-0022-2.

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Neuro-fuzzy control design of processes in chemical technologies The paper presents design of neuro-fuzzy control and its application in chemical technologies. Our approach to neuro-fuzzy control is a combination of the neural predictive controller and the neuro-fuzzy controller (Adaptive Network-based Fuzzy Inference System - ANFIS). These controllers work in parallel. The output of ANFIS adjusts the output of the neural predictive controller to enhance the control performance. Such design of an intelligent control system is applied to control of the continuous stirred tank reactor and laboratory mixing process.

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27

Parhi, DR, and S. Kundu. "Navigational strategy for underwater mobile robot based on adaptive neuro-fuzzy inference system model embedded with shuffled frog leaping algorithm–based hybrid learning approach." Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment 231, no. 4 (February 14, 2017): 844–62. http://dx.doi.org/10.1177/1475090216684235.

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In this research article, a novel navigational approach has been introduced for underwater robot based on learning and self-adaptation ability of adaptive neuro-fuzzy inference system. For avoiding obstacles during three-dimensional navigation, two adaptive neuro-fuzzy inference system models have been coupled to find out required change in heading angles of underwater robot in horizontal and vertical planes, respectively. A new hybrid learning scheme has been proposed for adaptive neuro-fuzzy inference system. Here, memetic approach based shuffled frog leaping algorithm has been used to tune the premise parameters and consequent parameters has been estimated through recursive least square estimation. Minimization of error in output of adaptive neuro-fuzzy inference system model has been treated as major objective of evolutionary-based training algorithm. Preliminary robotic behaviors of underwater robot have been successfully executed by implementing such well-trained adaptive neuro-fuzzy inference system architecture within three-dimensional unspecified workspace. Navigational performance of adaptive neuro-fuzzy inference system trained with the proposed hybrid learning algorithm has been compared with other three-dimensional navigational approaches in simulation mode for authentication purpose. Experimental verification has also been carried out to validate the feasibility and efficiency of the proposed navigational strategy.

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28

Ibnelouad, Aouatif, Abdeljalil Elkari, Hassan Ayad, and Mostafa Mjahed. "A neuro-fuzzy approach for tracking maximum power point of photovoltaic solar system." International Journal of Power Electronics and Drive Systems (IJPEDS) 12, no. 2 (June 1, 2021): 1252. http://dx.doi.org/10.11591/ijpeds.v12.i2.pp1252-1264.

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This work presents a hybrid soft-computing methodology approach for intelligent maximum power point tracking (MPPT) techniques of a photovoltaic (PV) system under any expected operating conditions using artificial neural network-fuzzy (neuro-fuzzy). The proposed technique predicts the calculation of the duty cycle ensuring optimal power transfer between the PV generator and the load. The neuro-fuzzy hybrid method combines artificial neural network (ANN) to direct the controller to the region where the MPP is located with its reference voltage estimator and its block of neural order. After that, the fuzzy logic controller (FLC) with rule inference begins to establish the photovoltaic solar system at the MPP. The obtained simulation results using MATLAB/simulink software for the proposed approach compared to ANN and the perturb and observe (P&O), proved that neuro-fuzzy approach fulfilled to extract the optimum power with pertinence, efficiency and precision

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29

BHATT, RAJEN B., and M. GOPAL. "NEURO-FUZZY DECISION TREES." International Journal of Neural Systems 16, no. 01 (February 2006): 63–78. http://dx.doi.org/10.1142/s0129065706000470.

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Fuzzy decision trees are powerful, top-down, hierarchical search methodology to extract human interpretable classification rules. However, they are often criticized to result in poor learning accuracy. In this paper, we propose Neuro-Fuzzy Decision Trees (N-FDTs); a fuzzy decision tree structure with neural like parameter adaptation strategy. In the forward cycle, we construct fuzzy decision trees using any of the standard induction algorithms like fuzzy ID3. In the feedback cycle, parameters of fuzzy decision trees have been adapted using stochastic gradient descent algorithm by traversing back from leaf to root nodes. With this strategy, during the parameter adaptation stage, we keep the hierarchical structure of fuzzy decision trees intact. The proposed approach of applying backpropagation algorithm directly on the structure of fuzzy decision trees improves its learning accuracy without compromising the comprehensibility (interpretability). The proposed methodology has been validated using computational experiments on real-world datasets.

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30

Carrascal, A., D. Manrique, J. Ríos, and C. Rossi. "Evolutionary Local Search of Fuzzy Rules through a Novel Neuro-Fuzzy Encoding Method." Evolutionary Computation 11, no. 4 (December 2003): 439–61. http://dx.doi.org/10.1162/106365603322519305.

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This paper proposes a new approach for constructing fuzzy knowledge bases using evolutionary methods. We have designed a genetic algorithm that automatically builds neuro-fuzzy architectures based on a new indirect encoding method. The neuro-fuzzy architecture represents the fuzzy knowledge base that solves a given problem; the search for this architecture takes advantage of a local search procedure that improves the chromosomes at each generation. Experiments conducted both on artificially generated and real world problems confirm the effectiveness of the proposed approach.

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31

Do, Quang Hung, and Jeng-Fung Chen. "A Neuro-Fuzzy Approach in the Classification of Students’ Academic Performance." Computational Intelligence and Neuroscience 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/179097.

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Classifying the student academic performance with high accuracy facilitates admission decisions and enhances educational services at educational institutions. The purpose of this paper is to present a neuro-fuzzy approach for classifying students into different groups. The neuro-fuzzy classifier used previous exam results and other related factors as input variables and labeled students based on their expected academic performance. The results showed that the proposed approach achieved a high accuracy. The results were also compared with those obtained from other well-known classification approaches, including support vector machine, Naive Bayes, neural network, and decision tree approaches. The comparative analysis indicated that the neuro-fuzzy approach performed better than the others. It is expected that this work may be used to support student admission procedures and to strengthen the services of educational institutions.

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32

Rajab, Sharifa. "Rule Base Simplification and Constrained Learning for Interpretability in TSK Neuro-Fuzzy Modelling." International Journal of Fuzzy System Applications 9, no. 2 (April 2020): 31–58. http://dx.doi.org/10.4018/ijfsa.2020040102.

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Neuro-fuzzy systems based on a fuzzy model proposed by Takagi, Sugeno and Kang known as the TSK fuzzy model provide a powerful method for modelling uncertain and highly complex non-linear systems. The initial fuzzy rule base in TSK neuro-fuzzy systems is usually obtained using data driven approaches. This process induces redundancy into the system by adding redundant fuzzy rules and fuzzy sets. This increases complexity which adversely affects generalization capability and transparency of the fuzzy model being designed. In this article, the authors explore the potential of TSK fuzzy modelling in developing comparatively interpretable neuro-fuzzy systems with better generalization capability in terms of higher approximation accuracy. The approach is based on three phases, the first phase deals with automatic data driven rule base induction followed by rule base simplification phase. Rule base simplification uses similarity analysis to remove similar fuzzy sets and resulting redundant fuzzy rules from the rule base, thereby simplifying the neuro-fuzzy model. During the third phase, the parameters of membership functions are fine-tuned using a constrained hybrid learning technique. The learning process is constrained which prevents unchecked updates to the parameters so that a highly complex rule base does not emerge at the end of model optimization phase. An empirical investigation of this methodology is done by application of this approach to two well-known non-linear benchmark forecasting problems and a real-world stock price forecasting problem. The results indicate that rule base simplification using a similarity analysis effectively removes redundancy from the system which improves interpretability. The removal of redundancy also increased the generalization capability of the system measured in terms of increased forecasting accuracy. For all the three forecasting problems the proposed neuro-fuzzy system demonstrated better accuracy-interpretability tradeoff as compared to two well-known TSK neuro-fuzzy models for function approximation.

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33

Pasila, Felix, Ajoy K. Palit, and Georg Thiele. "Neuro-Fuzzy Approaches for Forecasting Electrical Load Using Additional Moving Average Window Data Filter on Takagi-Sugeno Type MISO Networks." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 4 (July 20, 2008): 361–69. http://dx.doi.org/10.20965/jaciii.2008.p0361.

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The paper describes a neuro-fuzzy approach with additional moving average window data filter and fuzzy clustering algorithm that can be used to forecast electrical load using the Takagi-Sugeno (TS) type multi-input single-output (MISO) neuro-fuzzy network efficiently. The training algorithm with additional moving average filter is efficient in the sense that it can bring the performance index of the network, such as the sum squared error (SSE), down to the desired error goal much faster than the simple Levenberg-Marquardt algorithm (LMA). The fuzzy clustering algorithm allows the selection of initial parameters of fuzzy membership functions, e.g. mean and variance parameters of Gaussian membership functions of neuro-fuzzy networks, which are otherwise selected randomly. The initial parameters of fuzzy membership functions, which result in low SSE value with given training data of neuro-fuzzy network, are further fine tuned during the network training. Finally, the above training algorithm is tested on TS type MISO neuro-fuzzy structure for long-term forecasting application of electrical load time series.

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34

Al-wahbi, A. A., A. Abelrigeeb Al-gathe, and Ali Aldambi Abdulla. "NEURO-FUZZY APPROACH FOR GAS COMPRESSIBILITY FACTOR PREDICTION." Petroleum Engineering 20, no. 1 (March 2022): 45. http://dx.doi.org/10.17122/ngdelo-2022-1-45-52.

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35

Sajja, Priti Srinivas. "Intelligent Web Content Filtering through Neuro-Fuzzy Approach." International Journal Of Data Mining And Emerging Technologies 3, no. 1 (2013): 33. http://dx.doi.org/10.5958/j.2249-3220.3.1.002.

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36

Sezer, Alper, Burak A. Goktepe, and Selim Altun. "ADAPTIVE NEURO-FUZZY APPROACH FOR SAND PERMEABILITY ESTIMATION." Environmental Engineering and Management Journal 9, no. 2 (2010): 231–38. http://dx.doi.org/10.30638/eemj.2010.033.

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37

Das, Sudeb, and Malay Kumar Kundu. "A Neuro-Fuzzy Approach for Medical Image Fusion." IEEE Transactions on Biomedical Engineering 60, no. 12 (December 2013): 3347–53. http://dx.doi.org/10.1109/tbme.2013.2282461.

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38

Arafeh, L., H. Singh, and S. K. Putatunda. "A neuro fuzzy logic approach to material processing." IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 29, no. 3 (1999): 362–70. http://dx.doi.org/10.1109/5326.777072.

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39

Zurada, J., A. L. Wright, and J. H. Graham. "A neuro-fuzzy approach for robot system safety." IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 31, no. 1 (2001): 49–64. http://dx.doi.org/10.1109/5326.923268.

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40

Lazzerini, B., L. M. Reyneri, and M. Chiaberge. "A neuro-fuzzy approach to hybrid intelligent control." IEEE Transactions on Industry Applications 35, no. 2 (1999): 413–25. http://dx.doi.org/10.1109/28.753637.

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41

BENALI, R., N. DIB, and F. REGUIG BEREKSI. "CARDIAC ARRHYTHMIA DIAGNOSIS USING A NEURO-FUZZY APPROACH." Journal of Mechanics in Medicine and Biology 10, no. 03 (September 2010): 417–29. http://dx.doi.org/10.1142/s021951941000354x.

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The ventricular premature contractions (VPC) are cardiac arrhythmias that are widely encountered in the cardiologic field. They can be detected using the electrocardiogram (ECG) signal parameters. A novel method for detecting VPC from the ECG signal is proposed using a new algorithm (Slope) combined with a fuzzy-neural network (FNN). To achieve this objective, an algorithm for QRS detection is first implemented, and then a neuro-fuzzy classifier is developed. Its performances are evaluated by computing the percentages of sensitivity (SE), specificity (SP), and correct classification (CC). This classifier allows extraction of rules (knowledge base) to clarify the obtained results. We use the medical database (MIT-BIH) to validate our results.

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42

Bokshtein, Eli, Doron Shmaltz, Ophir Herbst, Horst Bunke, and Abraham Kandel. "Monopulse amplitude direction-finding using neuro-fuzzy approach." Robotics and Autonomous Systems 33, no. 2-3 (November 2000): 125–34. http://dx.doi.org/10.1016/s0921-8890(00)00083-x.

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43

Basak, Jayanta, Rajat K. De, and Sankar K. Pal. "Unsupervised feature selection using a neuro-fuzzy approach." Pattern Recognition Letters 19, no. 11 (September 1998): 997–1006. http://dx.doi.org/10.1016/s0167-8655(98)00083-x.

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44

Kazemian, H. B., and K. Ouazzane. "Neuro-Fuzzy approach to video transmission over ZigBee." Neurocomputing 104 (March 2013): 127–37. http://dx.doi.org/10.1016/j.neucom.2012.10.006.

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45

Lin, Yu-Chun, Shie-Jue Lee, Chen-Sen Ouyang, and Chih-Hung Wu. "Air quality prediction by neuro-fuzzy modeling approach." Applied Soft Computing 86 (January 2020): 105898. http://dx.doi.org/10.1016/j.asoc.2019.105898.

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46

Wang, W., F. Ismail, and F. Golnaraghi. "A Neuro-Fuzzy Approach to Gear System Monitoring." IEEE Transactions on Fuzzy Systems 12, no. 5 (October 2004): 710–23. http://dx.doi.org/10.1109/tfuzz.2004.834807.

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47

Hosseinzadeh Talaee, P. "Daily soil temperature modeling using neuro-fuzzy approach." Theoretical and Applied Climatology 118, no. 3 (January 9, 2014): 481–89. http://dx.doi.org/10.1007/s00704-013-1084-9.

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48

Akay, Diyar, and Mustafa Kurt. "A neuro-fuzzy based approach to affective design." International Journal of Advanced Manufacturing Technology 40, no. 5-6 (February 23, 2008): 425–37. http://dx.doi.org/10.1007/s00170-007-1367-3.

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49

Zhang, Bin, and Yung C. Shin. "A Data-Driven Approach of Takagi-Sugeno Fuzzy Control of Unknown Nonlinear Systems." Applied Sciences 11, no. 1 (December 23, 2020): 62. http://dx.doi.org/10.3390/app11010062.

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A novel approach to build a Takagi-Sugeno (T-S) fuzzy model of an unknown nonlinear system from experimental data is presented in the paper. The neuro-fuzzy models or, more specifically, fuzzy basis function networks (FBFNs) are trained from input–output data to approximate the nonlinear systems for which analytical mathematical models are not available. Then, the T-S fuzzy models are derived from the direct linearization of the neuro-fuzzy models. The operating points for linearization are chosen using the evolutionary strategy to minimize the global approximation error so that the T-S fuzzy models can closely approximate the original unknown nonlinear system with a reduced number of linearizations. Based on T-S fuzzy models, optimal controllers are designed and implemented for a nonlinear two-link flexible joint robot, which demonstrates the possibility of implementing the well-established model-based optimal control method onto unknown nonlinear dynamic systems.

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Biswas, Saroj Kr, Monali Bordoloi, Heisnam Rohen Singh, and Biswajit Purkayastha. "A Neuro-Fuzzy Rule-Based Classifier Using Important Features and Top Linguistic Features." International Journal of Intelligent Information Technologies 12, no. 3 (July 2016): 38–50. http://dx.doi.org/10.4018/ijiit.2016070103.

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Abstract:

The efficient feature selection for predictive and accurate classification is highly desirable in many application domains. Most of the attempts to neuro-fuzzy classifier lose information to build interpretable neuro-fuzzy classification model. This paper proposes an interpretable neuro-fuzzy classification model with significant features without loss of knowledge, which is an extension of an existing interpretable neuro-fuzzy classification model. The proposed model is designed based on the consideration of feature importance that is determined by frequency of linguistic features. The rules are then made based on important features. Therefore, the knowledge acquired in network can be comprehended to logical rules using only important features. The proposed model finally performs classification task by rule-based approach. The average accuracy calculated by 10-fold cross validation finds that the proposed model can increase performance of the already proven neuro-fuzzy system for classification tasks.

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