Relevant bibliographies by topics / Gaussian Membership function

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Gaussian Membership function'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Gaussian Membership function"

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Goyal, Paras, Sachin Arora, Dalchand Sharma, and Shruti Jain. "Design and Simulation of Gaussian Membership Function." IJIREEICE 4, no. 2 (2016): 26–28. http://dx.doi.org/10.17148/ijireeice.2016.4208.

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Wang, Yong, Cong Li, Hanqiao Huang, and Huan Zhou. "Selection of Fuzzy Controller Membership Functions Based on Adaptive Gaussian Cloud Transform." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 3 (2018): 439–47. http://dx.doi.org/10.1051/jnwpu/20183630439.

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Aiming at the boundedness of existing methods of selecting membership functions, an adaptive Gaussian cloud transform algorithm which is guided by the threshold values of hybridization degree is proposed to construct concept hierarchy from original sample data, and then the number, shape and coverage area of membership functions can be derived from the distribution of Gaussian cloud. To test and verify the effectiveness of membership function that is extracted based on adaptive Gaussian cloud transform algorithm, a six-degree-of freedom model of unmanned aerial vehicles(UAV) is constructed, an
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Ostapenko, R. O., and I. A. Hodashinsky. "Setting a rule base for a fuzzy classifier using the grasshopper optimization algorithm and the clustering algorithm." Proceedings of Tomsk State University of Control Systems and Radioelectronics 25, no. 2 (2022): 31–36. http://dx.doi.org/10.21293/1818-0442-2022-25-2-31-36.

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The article presents a description of a hybrid algorithm for generating fuzzy rules for a fuzzy classifier using grasshopper optimization algorithm and the K-means data clustering algorithm. The performance of clustering was evaluated by three fitness functions: total variance, Davis–Bouldin index, and Calinski–Harabasz index. Triangular and Gaussian membership functions have been investigated. The efficiency of the generated fuzzy rule bases has been tested on real datasets. The best combination is to use the total variance as the fitness function and the Gaussian function as the membership f
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Yang, Shao Zeng, and Jian Hua Zhang. "A Robust Operator Functional State Fuzzy Modeling Approach Based on EEG Data." Applied Mechanics and Materials 556-562 (May 2014): 4065–68. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4065.

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Operator functional state (OFS) is defined as the time-variable ability that an operator completes his/her assigned tasks. To evaluate the OFS in safety-critical human-machine systems, it is modeled by using the Wang-Mendel-based fuzzy system paradigm in this paper. The fuzzy model is constructed to correlate three EEG features (as model inputs) to the human-machine system performance (as model output). To derive a fuzzy model for real-time OFS assessment, the Gaussian membership function membership crossover point membership gradeδis found to be an essential parameter that controls the robust
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Khairuddin, Siti Hajar, Mohd Hilmi Hasan, Manzoor Ahmed Hashmani, and Muhammad Hamza Azam. "Generating Clustering-Based Interval Fuzzy Type-2 Triangular and Trapezoidal Membership Functions: A Structured Literature Review." Symmetry 13, no. 2 (2021): 239. http://dx.doi.org/10.3390/sym13020239.

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Clustering is more popular than the expert knowledge approach in Interval Fuzzy Type-2 membership function construction because it can construct membership function automatically with less time consumption. Most research proposed a two-fuzzifier fuzzy C-Means clustering method to construct Interval Fuzzy Type-2 membership function which mainly focused on producing Gaussian membership function. The other two important membership functions, triangular and trapezoidal, are constructed using the grid partitioning method. However, the method suffers a drawback of not being able to represent actual
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Mitsuishi, Takashi. "Some Properties of Membership Functions Composed of Triangle Functions and Piecewise Linear Functions." Formalized Mathematics 29, no. 2 (2021): 103–15. http://dx.doi.org/10.2478/forma-2021-0011.

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Summary. IF-THEN rules in fuzzy inference is composed of multiple fuzzy sets (membership functions). IF-THEN rules can therefore be considered as a pair of membership functions [7]. The evaluation function of fuzzy control is composite function with fuzzy approximate reasoning and is functional on the set of membership functions. We obtained continuity of the evaluation function and compactness of the set of membership functions [12]. Therefore, we proved the existence of pair of membership functions, which maximizes (minimizes) evaluation function and is considered IF-THEN rules, in the set o
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A. Waly, Mohamed, Ibrahim F. Tarrad, and Mohamed M. Fouad. "Surge Detection System Using Gaussian Curve Membership Function." International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering 03, no. 11 (2014): 12811–18. http://dx.doi.org/10.15662/ijareeie.2014.0311003.

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OTA, Y., and B. M. WILAMOWSKI. "CMOS IMPLEMENTATION OF A VOLTAGE-MODE FUZZY MIN-MAX CONTROLLER." Journal of Circuits, Systems and Computers 06, no. 02 (1996): 171–84. http://dx.doi.org/10.1142/s0218126696000145.

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In this paper, a general-purpose fuzzy min-max network using a Gaussian-type membership function fuzzifier is proposed. Particularly, CMOS implementations of the Gaussian-type membership function fuzzifier circuits, min-max operators, and the defuzzifier circuit are analyzed. Programmability of the proposed Gaussian-type function fuzzifier can be achieved by changing the gate voltages and the sizes of transistors in the differential pairs. A closed-loop control scheme is used between the fuzzifier and defuzzifier blocks to compensate the global normalization of the denominator in the division
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Eyoh, I. J., U. A. Umoh, U. G. Inyang, and O. S. Adeoye. "Elliptic interval Type-2 intuitionistic fuzzy logic system for non-linear system identification." World Journal of Applied Science & Technology 15, no. 1 (2023): 48–54. http://dx.doi.org/10.4314/wojast.v15i1.48.

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An elliptic membership function has been proposed in the literature for interval type-2 fuzzy logic system. In this paper, elliptic non- membership function is incorporated into the conventional elliptic membership function model to obtain elliptic interval type-2 intuitionistic fuzzy sets for the first time. The elliptic interval type-2 intuitionistic fuzzy logic system so formed is applied for the prediction of two benchmark non-linear systems and results compared with Gaussian interval type-2 intuitionistic fuzzy logic system. Experimental results show that the elliptic interval type-2 intu
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Kuo, R. J., and W. C. Cheng. "An intuitionistic fuzzy neural network with gaussian membership function." Journal of Intelligent & Fuzzy Systems 36, no. 6 (2019): 6731–41. http://dx.doi.org/10.3233/jifs-18998.

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Dissertations / Theses on the topic "Gaussian Membership function"

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Lin, Rui-Jie, and 林瑞杰. "Additive Gaussian Membership Functions in Fuzzy Neural Network." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/55615490855259126785.

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碩士<br>國立交通大學<br>電機與控制工程系<br>88<br>In this thesis, a new method to tune the membership functions of fuzzy neural network (FNN) is presented. First we study the FNN it inherits the property of both fuzzy inference system and neural network. Then we present that any gaussian function can be represented by the linear combination of gaussian functions with small standard deviation. Therefore, it can be substituted for the second layer of FNN (called FNN5). We use the FNN5 to approximate some functions and prove that it is a universal approximator. Furthermore, apply this proposed method to tune PI
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Book chapters on the topic "Gaussian Membership function"

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Bharatraj, Janani. "Interval Valued Intuitionistic Fuzzy Gaussian Membership Function: A Novel Extension." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51156-2_44.

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Benitez-Diaz, D., and J. Garcia-Quesada. "Learning algorithm with gaussian membership function for Fuzzy RBF Neural Networks." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-59497-3_219.

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Pieczyński, Andrzej, and Andrzej Obuchowicz. "Application of the General Gaussian Membership Function for the Fuzzy Model Parameters Tunning." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24844-6_50.

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Gopal, Smriti Srivastava, Saurabh Bhardwaj, and Preet Kiran. "Gaussian Membership Function-Based Speaker Identification Using Score Level Fusion of MFCC and GFCC." In Proceedings of the International Congress on Information and Communication Technology. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0767-5_31.

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Ma, Weimin, and Guoqing Chen. "Improvement on the Approximation Bound for Fuzzy-Neural Networks Clustering Method with Gaussian Membership Function." In Advanced Data Mining and Applications. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11527503_27.

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Madhu, Golla. "Gaussian Membership Function and Type II Fuzzy Sets Based Approach for Edge Enhancement of Malaria Parasites in Microscopic Blood Images." In Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-00665-5_64.

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Naumoski, Andreja, Georgina Mirceva, and Kosta Mitreski. "Diatom Ecological Modelling with Weighted Pattern Tree Algorithm by Using Polygonal and Gaussian Membership Functions." In Communications in Computer and Information Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33110-8_8.

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Gaxiola, Fernando, Patricia Melin, and Fevrier Valdez. "Neural Network with Fuzzy Weights Using Type-1 and Type-2 Fuzzy Learning with Gaussian Membership Functions." In Studies in Computational Intelligence. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05170-3_4.

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Gill, Harmandeep Singh, and Baljit Singh Khehra. "A Novel Type-II Fuzzy based Fruit Image Enhancement Technique Using Gaussian S-shaped and Z-Shaped Membership Functions." In Algorithms for Intelligent Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3246-4_1.

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Saha, Sriparna, and Amit Konar. "A Study on Static Hand Gesture Recognition Using Type-1 Fuzzy Membership Function." In Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3129-6.ch005.

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The idea of this chapter is the use of Gaussian type-1 fuzzy membership functions based approach for automatic hand gesture recognition. The process has been carried out in five stages starting with the use of skin color segmentation for the isolation of the hand from the background. Then Sobel edge detection technique is employed to extract the contour of the hand. The next stage comprises of the calculation of eight spatial distances by locating the center point of the boundary and all distances are normalized with respect to the maximum distance value. Finally, matching based on Gaussian fu
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Conference papers on the topic "Gaussian Membership function"

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Kaur, Gurpreet, and Gurmeet Kaur. "Fuzzy-Neuro Network in a CO-OFDM system: Various Membership Functions Comparison." In International Conference on Women Researchers in Electronics and Computing. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.114.46.

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Fuzzy-Neuro Network based nonlinear equalizer (FNN-NLE) has been used for the extenuation of nonlinearities in optical communication systems. Until now, many membership functions with resilient backpropagation activation function was used for making FNN-NLE in a coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems. Despite this, no research is reflecting the comparison of different membership functions (MFs). In this paper, various membership functions such as gaussian MF, gaussian combination MF, triangular MF, difference between two sigmoidal functions MF, pi shaped
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Kulkarni, U. V., and S. V. Shinde. "Neuro-fuzzy classifier based on the Gaussian membership function." In 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT). IEEE, 2013. http://dx.doi.org/10.1109/icccnt.2013.6726629.

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Singh, Sumeet, and Harpreet Kaur. "Energy aware Internet of Things using Gaussian membership function." In 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, 2016. http://dx.doi.org/10.1109/pdgc.2016.7913138.

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Aljawarneh, Shadi A., V. Radhakrishna, and Aravind Cheruvu. "Extending the Gaussian membership function for finding similarity between temporal patterns." In 2017 International Conference on Engineering & MIS (ICEMIS). IEEE, 2017. http://dx.doi.org/10.1109/icemis.2017.8273100.

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Biswas, Priyankar, and Kalyan Kumar Halder. "Speckle Noise Reduction from Medical Images Using Gaussian Fuzzy Membership Function." In 2021 3rd International Conference on Electrical & Electronic Engineering (ICEEE). IEEE, 2021. http://dx.doi.org/10.1109/iceee54059.2021.9718944.

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Cordeiro, Filipe R., Beatriz Albuquerque, and Valmir Macario. "A Multi-Gaussian Fuzzy Membership Function to the Algorithm Fuzzy GrowCut Applied to Segment Lesions in Mammography Images." In XV Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/eniac.2018.4426.

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Segmentation of masses in mammography images is an important task to aid the accurate diagnosis of breast cancer. Although the quality of segmentation is crucial to avoid misdiagnosis, the segmentation process is a challenging task even for specialists, due to the presence of ill-defined edges and low contrast images. One of the techniques of state of the art for tumor segmentation is the Fuzzy GrowCut algorithm. In this work a study is performed on the behavior of this algorithm when using different membership functions for segmentation. Moreover, this research proposes a new membership funct
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Moradi Khaneshan, Tohid, Mojdeh Nematzadeh, Abdollah Khoei, and Khayrollah Hadidi. "An analog reconfigurable Gaussian-shaped membership function generator using current-mode techniques." In 2012 20th Iranian Conference on Electrical Engineering (ICEE). IEEE, 2012. http://dx.doi.org/10.1109/iraniancee.2012.6292341.

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Saatlo, Ali Naderi, and Serdar Ozoguz. "On the realization of Gaussian membership function circuit operating in saturation region." In 2015 38th International Conference on Telecommunications and Signal Processing (TSP). IEEE, 2015. http://dx.doi.org/10.1109/tsp.2015.7296419.

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Huynh, Tuan-Tu, Chih-Min Lin, Tien-Loc Le, and Zhixiong Zhong. "A Mixed Gaussian Membership Function Fuzzy CMAC for a Three-Link Robot." In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2020. http://dx.doi.org/10.1109/fuzz48607.2020.9177761.

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Biswas, Priyankar, Kalyan Kumar Halder, and Arnab Sarkar. "Modified Gaussian Fuzzy Membership Function for Mixed Noise Reduction from Ultrasound Images." In 2022 International Conference on Recent Progresses in Science, Engineering and Technology (ICRPSET). IEEE, 2022. http://dx.doi.org/10.1109/icrpset57982.2022.10188550.

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Reports on the topic "Gaussian Membership function"

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Tsidylo, Ivan M., Serhiy O. Semerikov, Tetiana I. Gargula, Hanna V. Solonetska, Yaroslav P. Zamora, and Andrey V. Pikilnyak. Simulation of intellectual system for evaluation of multilevel test tasks on the basis of fuzzy logic. CEUR Workshop Proceedings, 2021. http://dx.doi.org/10.31812/123456789/4370.

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The article describes the stages of modeling an intelligent system for evaluating multilevel test tasks based on fuzzy logic in the MATLAB application package, namely the Fuzzy Logic Toolbox. The analysis of existing approaches to fuzzy assessment of test methods, their advantages and disadvantages is given. The considered methods for assessing students are presented in the general case by two methods: using fuzzy sets and corresponding membership functions; fuzzy estimation method and generalized fuzzy estimation method. In the present work, the Sugeno production model is used as the closest
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