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Journal articles on the topic 'Gaussian Membership function'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

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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Basterretxea, K., J. M. Tarela, and I. del Campo. "Digital Gaussian membership function circuit for neuro-fuzzy hardware." Electronics Letters 42, no. 1 (2006): 44. http://dx.doi.org/10.1049/el:20063712.

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Liu, Xiang-Jie, and Xiao-Xin Zhou. "Structural analysis of fuzzy controller with gaussian membership function." IFAC Proceedings Volumes 32, no. 2 (1999): 5368–73. http://dx.doi.org/10.1016/s1474-6670(17)56914-1.

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Shanthi, S. Anita, and N. Keerthana. "Evaluating types of soil suitable for construction using radial basis function fuzzy neural network." E3S Web of Conferences 405 (2023): 04030. http://dx.doi.org/10.1051/e3sconf/202340504030.

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This paper deals with radial basis fuzzy neural network using bipolar picture fuzzy soft sets. Firstly a bipolar picture fuzzy decision matrix is constructed. A bipolar picture fuzzy Gaussian membership function is defined. Next degree values are calculated and used for finding the entropy. These entropy values help in determining the weights of bipolar picture fuzzy sets. Output is obtained by multiplication of weight values and BPFS Gaussian membership values. By using this method, the soil suitable for construction of buildings is found.

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Aseel Sameer Mohammed. "Suggested Method for Prediction Using Gaussian Process Regression Kernel Regression." Journal of Information Systems Engineering and Management 10, no. 35s (2025): 721–34. https://doi.org/10.52783/jisem.v10i35s.6280.

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Accurately predicting children's weight is challenging due to measurement inconsistencies. To address this, a hybrid kernel function, combining the squared exponential kernel and the Gaussian kernel with a mixture parameter, is proposed for developing a fuzzy Gaussian process regression model. The integration of fuzzy set theory and a triangular membership function helps handle weight measurement inaccuracies by determining the degrees of membership for each element in the weight vector. The model is estimated using the spider monkey optimization (SMO) algorithm and implemented in MATLAB Ver.

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Nezar, Ismat Seno, Abdul Wahed Salman Muntaser, and Nory Farhan Rabah. "A comparative study of multiband mamdani fuzzy classification methods for west of Iraq satellite image." Bulletin of Electrical Engineering and Informatics 11, no. 3 (2022): 1624~1632. https://doi.org/10.11591/eei.v11i3.3561.

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In our paper, performance of four fuzzy membership function generation methods was studied. These methods were studied in the context of implementing Mamdani fuzzy classification on a set of satellite images for western Iraqi territory. The first method generate triangulate membership functions using mean, minimum (min) and maximum (max) of histogram attribute values (AV), while peak and standard deviation (STD) of these AV were used in the second. On the other hand, in the third and fourth methods, Gaussian membership functions are generated using same mentioned values in the first and second

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Seno, Nezar Ismat, Muntaser Abdul Wahed Salman, and Rabah Nory Farhan. "A comparative study of multiband mamdani fuzzy classification methods for west of Iraq satellite image." Bulletin of Electrical Engineering and Informatics 11, no. 3 (2022): 1624–32. http://dx.doi.org/10.11591/eei.v11i3.3561.

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In our paper, performance of four fuzzy membership function generation methods was studied. These methods were studied in the context of implementing Mamdani fuzzy classification on a set of satellite images for western Iraqi territory. The first method generate triangulate membership functions using mean, minimum (min) and maximum (max) of histogram attribute values (AV), while peak and standard deviation (STD) of these AV were used in the second. On the other hand, in the third and fourth methods, Gaussian membership functions are generated using same mentioned values in the first and second

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Azimi, S. M., and H. Miar-Naimi. "Designing programmable current-mode Gaussian and bell-shaped membership function." Analog Integrated Circuits and Signal Processing 102, no. 2 (2019): 323–30. http://dx.doi.org/10.1007/s10470-019-01567-y.

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Mykhail, Gorbiychuk, Kropyvnytskyi Dmytro, and Kropyvnytska Vitalia. "Improving empirical models of complex technological objects under conditions of uncertainty." Eastern-European Journal of Enterprise Technologies 2, no. 2(122) (2023): 53–63. https://doi.org/10.15587/1729-4061.2023.276586.

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This paper proposes a method for improving empirical models of complex technological objects with insufficient information about the input and output values of an object's parameters. It has been established that most methods for constructing empirical models require knowledge of the statistical characteristics of the input and output values of an object. When modeling complex non-reproducible stochastic processes that evolve over time, information about the parameters and structure of an object is usually not available. A method has been proposed where input and output values are treated

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Gideon Alfano, Anrico, Hari Maghfiroh, Irwan Iftadi, Chico Hermanu B.A., and Feri Adriyanto. "Modelling and Simulation of DC Motor Speed Control Using Fuzzy-PID Algorithm." Journal of Electrical, Electronic, Information, and Communication Technology 1, no. 1 (2019): 13. http://dx.doi.org/10.20961/jeeict.v1i1.34260.

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<p class="Abstract">This paper discusses the simulation of the Fuzzy-PID hybrid algorithm as a method for controlling the speed of a DC motor compared to the usual PID method. Comparisons were also made to the membership functions used in the fuzzy logic fuzzification process. Membership functions that are used are triangular, trapezoidal, and gaussian shaped function with each having 3, 5, and 7 as the number of membership to be compared. The performance of this control is compared by looking at the results of the step unit responses and the ability in tracking signals. The simulation r

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Gogo, Kevin Otieno, Lawrence Nderu, and Makau Mutua. "Variances in knowledge-based interval type 2 Gaussian fuzzy on linear regression models." Journal of Intelligent & Fuzzy Systems 41, no. 1 (2021): 1807–20. http://dx.doi.org/10.3233/jifs-210568.

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Fuzzy logic is a branch of artificial intelligence that has been used extensively in developing Fuzzy systems and models. These systems usually offer artificial intelligence based on the predictive mathematical models used; in this case linear regression mathematical model. Interval type 2 Gaussian fuzzy logic is a fuzzy logic that utilizes Gaussian upper membership function and the lower membership function, with a footprint of uncertainty in between the Gaussian membership functions. The artificial intelligence solutions predicted by these interval type 2 fuzzy systems depends on the trainin

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Shi, Jianzhong. "Identification of Circulating Fluidized Bed Boiler Bed Temperature Based on Hyper-Plane-Shaped Fuzzy C-Regression Model." International Journal of Computational Intelligence and Applications 19, no. 04 (2020): 2050029. http://dx.doi.org/10.1142/s1469026820500297.

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Bed temperature in dense-phase zone is the key parameter of circulating fluidized bed (CFB) boiler for stable combustion and economic operation. It is difficult to establish an accurate bed temperature model as the complexity of circulating fluidized bed combustion system. T-S fuzzy model was widely applied in the system identification for it can approximate complex nonlinear system with high accuracy. Fuzzy c-regression model (FCRM) clustering based on hyper-plane-shaped distance has the advantages in describing T-S fuzzy model, and Gaussian function was adapted in antecedent membership funct

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Azam, Muhammad Hamza, Mohd Hilmi Hasan, Saima Hassan, and Said Jadid Abdulkadir. "A Novel Approach to Generate Type-1 Fuzzy Triangular and Trapezoidal Membership Functions to Improve the Classification Accuracy." Symmetry 13, no. 10 (2021): 1932. http://dx.doi.org/10.3390/sym13101932.

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Fuzzy logic is an approach that reflects human thinking and decision making by handling uncertainty and vagueness using fuzzy membership functions. When a human is engaged in the design of a fuzzy system, symmetric properties are naturally preferred. Fuzzy c-means clustering is a clustering algorithm that can cluster datasets to produce membership matrix and cluster centers, which results in generating type-1 fuzzy membership functions. However, fuzzy c-means algorithm has a limitation of producing only a single membership function type, Gaussian MF. Generation of multiple fuzzy membership fun

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Abdulla, K. P., and Mohammad Fazle Azeem. "A Novel Programmable CMOS Fuzzifiers Using Voltage-to-Current Converter Circuit." Advances in Fuzzy Systems 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/419370.

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This paper presents a new voltage-input, current-output programmable membership function generator circuit (MFC) using CMOS technology. It employs a voltage-to-current converter to provide the required current bias for the membership function circuit. The proposed MFC has several advantageous features. This MFC can be reconfigured to perform triangular, trapezoidal, S-shape, Z-Shape, and Gaussian membership forms. This membership function can be programmed in terms of its width, slope, and its center locations in its universe of discourses. The easily adjustable characteristics of the proposed

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Kundu, Krishan. "Image Denoising using Patch based Processing with Fuzzy Gaussian Membership Function." International Journal of Computer Applications 118, no. 12 (2015): 35–40. http://dx.doi.org/10.5120/20799-3474.

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Abdullah, Suhaila N., and Iden Hasan Huessian. "Estimate the parameters of Weibull distribution by using nonlinear membership function by Gaussian function." Journal of Physics: Conference Series 1591 (July 2020): 012040. http://dx.doi.org/10.1088/1742-6596/1591/1/012040.

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Barraza, Juan, Patricia Melin, Fevrier Valdez, and Claudia I. Gonzalez. "Modeling of Fuzzy Systems Based on the Competitive Neural Network." Applied Sciences 13, no. 24 (2023): 13091. http://dx.doi.org/10.3390/app132413091.

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This paper presents a method to dynamically model Type-1 fuzzy inference systems using a Competitive Neural Network. The aim is to exploit the potential of Competitive Neural Networks and fuzzy logic systems to generate an intelligent hybrid model with the ability to group and classify any dataset. The approach uses the Competitive Neural Network to cluster the dataset and the fuzzy model to perform the classification. It is important to note that the fuzzy inference system is generated automatically from the classes and centroids obtained with the Competitive Neural Network, namely, all the p

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Zhang, Yun, and Chaoxia Qin. "A Gaussian-Shaped Fuzzy Inference System for Multi-Source Fuzzy Data." Systems 10, no. 6 (2022): 258. http://dx.doi.org/10.3390/systems10060258.

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Fuzzy control theory has been extensively used in the construction of complex fuzzy inference systems. However, we argue that existing fuzzy control technologies focus mainly on the single-source fuzzy information system, disregarding the complementary nature of multi-source data. In this paper, we develop a novel Gaussian-shaped Fuzzy Inference System (GFIS) driven by multi-source fuzzy data. To this end, we first propose an interval-value normalization method to address the heterogeneity of multi-source fuzzy data. The contribution of our interval-value normalization method involves mapping

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Sahu, Supriya, and Bibhuti Bhusan Choudhury. "Fuzzy Logic Based Path Planning for Industrial Robot." International Journal of Manufacturing, Materials, and Mechanical Engineering 8, no. 3 (2018): 1–11. http://dx.doi.org/10.4018/ijmmme.2018070101.

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This article describes how industrial robots are generally used to perform different tasks in industries, such as pick and place, and many more operations in industries. Among these, pick and place is a very common and frequently used task. Path planning is the most important thing in order to make any process more economical. The main focus of the research is to design a fuzzy control system for path planning for industrial robots using artificial intelligence using fuzzy logic. For the analysis, ten different tasks are tested. For fuzzy logic systems, three membership functions are analyzed

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Anggara, Yogi, and Arif Munandar. "Implementation of Hybrid RNN-ANFIS on Forecasting Jakarta Islamic Index." Jambura Journal of Mathematics 5, no. 2 (2023): 419–30. http://dx.doi.org/10.34312/jjom.v5i2.20407.

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RNN is a type of artificial neural network used to handle problems that require sequential data processing. ANFIS is a method that combines the advantages of fuzzy logic and artificial neural networks to create a system, so can adapt the parameters it uses according to the obtained data so that it can build an automated inference system. In this research, we make combination of RNN in ANFIS, which makes ANFIS able to accept input in the form of time series data so that ANFIS can recognize patterns contained in the time series data and its suitable for forecasting cases in the Jakarta Islamic I

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Muludi, Kurnia, Revita Setianingsih, Ridho Sholehurrohman, and Akmal Junaidi. "Exploiting nearest neighbor data and fuzzy membership function to address missing values in classification." PeerJ Computer Science 10 (March 28, 2024): e1968. http://dx.doi.org/10.7717/peerj-cs.1968.

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The accuracy of most classification methods is significantly affected by missing values. Therefore, this study aimed to propose a data imputation method to handle missing values through the application of nearest neighbor data and fuzzy membership function as well as to compare the results with standard methods. A total of five datasets related to classification problems obtained from the UCI Machine Learning Repository were used. The results showed that the proposed method had higher accuracy than standard imputation methods. Moreover, triangular method performed better than Gaussian fuzzy me

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Fauzi, Fatkhurokhman, Relly Erlinda, and Prizka Rismawati Arum. "Rainfall Forecasting Using an Adaptive Neuro-Fuzzy Inference System with a Grid Partitioning Approach to Mitigating Flood Disasters." JTAM (Jurnal Teori dan Aplikasi Matematika) 8, no. 2 (2024): 520. http://dx.doi.org/10.31764/jtam.v8i2.20385.

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Hydrometeorological disasters are one of the disasters that often occur in big cities like Semarang. Hydrometeorological disasters that often occur are floods caused by high-intensity rainfall in the area. Early mitigation needs to be done by knowing about future rain. Rainfall data in Semarang City fluctuates, so the Adaptive Neuro-Fuzzy Inference System (ANFIS) method approach is very appropriate. This research will use the Grid Partitioning (GP) approach to produce more accurate forecasting. The data used in this research is daily rainfall observation data from the Meteorology Climatology G

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Wang, Yizhi, Yusen Zhang, Fengyuan Ma, et al. "Research on Precise Feeding Strategies for Large-Scale Marine Aquafarms." Journal of Marine Science and Engineering 12, no. 9 (2024): 1671. http://dx.doi.org/10.3390/jmse12091671.

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Breeding in large-scale marine aquafarms faces many challenges in terms of precise feeding, including real-time decisions as to the precise feeding amount, along with disturbances caused by the feeding speed and the moving speed of feeding equipment. Involving many spatiotemporal distributed parameters and variables, an effective predictive model for environment and growth stage perception is yet to obtained, further preventing the development of precise feeding strategies and feeding equipment. Therefore, in this paper, a hierarchical type-2 fuzzy system based on a quasi-Gaussian membership f

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Gupta, Aman, Shilpa Pal, and Alok Verma. "Fuzzy Logic Approach in Determination of Strength in Concrete." INTERNATIONAL JOURNAL OF ADVANCED PRODUCTION AND INDUSTRIAL ENGINEERING 3, no. 1 (2018): 39–47. http://dx.doi.org/10.35121/ijapie201801129.

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The aim of this thesis is to address capabilities in the prediction of compressive strength of concrete to affect quality control in construction. To comprehend this, a compressive strength predicting model using the principles of fuzzy logic set theory had been employed. The model put into use ‘fuzzy logic’ as a tool to predict the compressive strength of concrete on a given day. Data collected from previous researches and laboratory work had been put into use in the model construction and testing. The input variables of water/binder ratio, cement content, water content, and fly ash percentag

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Wahyu, Ari Purno, and Umi Hayati. "PENENTUAN JURUSAN PADA PROSES PENERIMAAN MAHASISWA DENGAN PENDEKATAN LOGIKA FUZZY." Jurnal Ilmiah Teknologi Infomasi Terapan 8, no. 1 (2021): 193–200. http://dx.doi.org/10.33197/jitter.vol8.iss1.2021.747.

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Choosing a major is not an easy matter. There are many factors that must be taken into account and carefully thought out so that in choosing a major, it will cause big losses. There are many ways to determine the selection of majors, one of which is by using fuzzy logic. Determination of majors is determined from the results of the selection test in the academic field with the subjects of Mathematics, English and Computer Knowledge. With the aim of recommending the selection of the right major according to academic abilities and improving quality. By using the fuzzy inference model Mamdani max

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Awogbami, Gabriel, and Abdollah Homaifar. "A Reliability-Based Multisensor Data Fusion with Application in Target Classification." Sensors 20, no. 8 (2020): 2192. http://dx.doi.org/10.3390/s20082192.

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The theory of belief functions has been extensively utilized in many practical applications involving decision making. One such application is the classification of target based on the pieces of information extracted from the individual attributes describing the target. Each piece of information is usually modeled as the basic probability assignment (BPA), also known as the mass function. The determination of the BPA has remained an open problem. Although fuzzy membership functions such as triangular and Gaussian functions have been widely used to model the likelihood estimation function based

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Bisyanov, E. E., and A. A. Gutnik. "METHOD FOR OBTAINING THE PARAMETERS OF MEMBERSHIP FUNCTIONS OF FUZZY SETS BASED ON REAL DATA FOR AUTOMATED INFORMATION PROCESSING SYSTEMS." Herald of Dagestan State Technical University. Technical Sciences 46, no. 3 (2019): 79–86. http://dx.doi.org/10.21822/2073-6185-2019-46-3-79-86.

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Objectives Development of a method for selecting the type of accessory function and obtaining its parameters to allow subjective personal influences in automated information processing to be excluded.Method. Existing methods for constructing membership functions were analysed. The research was based on the methods of fuzzy logic and data analysis.Results. A method for obtaining the parameters of membership functions of fuzzy sets using real data is suggested. It is proposed to use the data obtained from the object under study to determine the kernel of the fuzzy number, as well as derive theor

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Hussein, Dr Abdullah Al. "A Hybrid Gaussian Membership Function (GMF) and Fuzzy based Cost Drivers for Effective Software Cost Estimation: An Application Software Perspective." Journal of Advanced Research in Dynamical and Control Systems 12, no. 8 (2020): 56–66. http://dx.doi.org/10.5373/jardcs/v12i8/20202446.

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Dhaundiyal, Alok, and Suraj B. Singh. "Analysis of the non Isothermal Distributed Activation Energy Model for Biomass Pyrolysis by Fuzzy Gaussian Distribution." Rural Sustainability Research 35, no. 330 (2016): 32–41. http://dx.doi.org/10.1515/plua-2016-0005.

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Abstract The main aim of this paper is to fuzzify the kinetic parameters, which have crisp nature, in order to obtain the realistic and accurate results. In the present study, the variance, upper limit of ‘dE’ and the frequency factor are assumed to be fuzzy numbers. The Gaussian distribution is considered as the distribution function, f (E), of Distributed Activation Energy Model (DAEM). The membership and the non-membership functions are evaluated by the trapezoidal fuzzy number. Thermo-analytical data has been found experimentally with the help of TGA/DTG analysis. The approximated solution

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Surono, Sugiyarto, Tia Nursofiyani, and Annisa E. Haryati. "Optimization of Fuzzy Support Vector Machine (FSVM) Performance by Distance-Based Similarity Measure Classification." HighTech and Innovation Journal 2, no. 4 (2021): 285–92. http://dx.doi.org/10.28991/hij-2021-02-04-02.

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This research aims to determine the maximum or minimum value of a Fuzzy Support Vector Machine (FSVM) Algorithm using the optimization function. SVM is considered as an effective method of data classification, as opposed to FSVM, which is less effective on large and complex data because of its sensitivity to outliers and noise. One of the techniques used to overcome this inefficiency is fuzzy logic with its ability to select the right membership function, which significantly affects the effectiveness of the FSVM algorithm performance. This research was carried out using the Gaussian membership

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JANEELA THERESA, M. M., and V. JOSEPH RAJ. "MODIFIED FUZZY NEURAL NETWORK FOR THE CLASSIFICATION OF MURDER CASES IN CRIMINAL LAW USING GAUSSIAN MEMBERSHIP FUNCTION." International Journal of Computational Intelligence and Applications 12, no. 02 (2013): 1350011. http://dx.doi.org/10.1142/s1469026813500119.

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This paper presents the problem of decision making by a judge in the case of murder cases in criminal law using single hidden layered fuzzy neural network algorithm. Since the membership functions (MFs) of fuzzy sets can affect the performance of the classification models, determination of MFs is crucial. In this paper, the MF selected is Triangular and Gaussian is proposed for evaluation to improve the classification results. To evaluate the effectiveness of the proposed Fuzzy Neural Network model for the classification of murder cases, sufficient number of real-world data sets of court decis

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Sedana Wijaya, I. Putu, Made Agung Raharja, Luh Arida Ayu Rahning Putri, I. Putu Gede Hendra Suputra, Ida Bagus Made Mahendra, and I. Gede Santi Astawa. "Penerapan Metode Adaptive Neuro Fuzzy Inference System (ANFIS) Dengan Membership function Tipe Gaussian dan Generalized Bell Dalam Prediksi Harga Tertinggi Saham." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 11, no. 1 (2022): 111. http://dx.doi.org/10.24843/jlk.2022.v11.i01.p12.

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Banyak kalangan yang memiliki modal saat ini beramai-ramai memborong saham di stock market dengan harapan harga saham tersebut akan naik saat pandemi Covid-19 berakhir. Sebagai seseorang yang ingin mencoba berinvestasi di stock market harus mampu memperkirakan untung dan rugi dari pembelian saham. Salah satu cara yang dapat membantu pertimbangan dalam pegambilan keputusan membeli dan menjual saham adalah melakukan prediksi. Terdapat banyak algoritma yang dapat digunakan dalam prediksi salah satunya adalah metode Adaptive Neuro Fuzzy Inference System (ANFIS) yang merupakan penggabungan dari alg

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Zakovorotniy, Alexander, and Artem Kharchenko. "Properties of interval type-2 fuzzy sets in decision support systems." Bulletin of the National Technical University «KhPI» Series: New solutions in modern technologies, no. 4 (10) (December 30, 2021): 75–81. http://dx.doi.org/10.20998/2413-4295.2021.04.10.

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Definitions and methods of designing interval type-2 fuzzy sets in fuzzy inference systems for control problems of complex technical objects in conditions of uncertainty are considered. The main types of uncertainties, that arise when designing fuzzy inference systems and depend on the number of expert assessments, are described. Methods for assessing intra-uncertainty and inter-uncertainty are proposed, taking into account the different number of expert assessments at the stage of determining the types and number of membership functions. Factors influencing the parameters and properties of in

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Lin, Bor Tsuen, Kun Min Huang, and Chun Chih Kuo. "An Adaptive-Network-Based Fuzzy Inference System for Predicting Springback of U-Bending." Applied Mechanics and Materials 284-287 (January 2013): 25–30. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.25.

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Springback will occur when the external force is removed after bending process in sheet metal forming. This paper proposed an adaptive-network-based fuzzy inference system (ANFIS) model for prediction the springback angle of the SPCC material after U-bending. Three parameters were selected as the main factors of affecting the springback after bending, including the die clearance, the punch radius, and the die radius. The training data were obtained from results of U-bending experiment. The training data with four different membership functions – triangular, trapezoidal, bell, and Gaussian func

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Lin, Bor-Tsuen, and Kun-Min Huang. "AN ADAPTIVE-NETWORK-BASED FUZZY INFERENCE SYSTEM FOR PREDICTING SPRINGBACK OF U-BENDING." Transactions of the Canadian Society for Mechanical Engineering 37, no. 3 (2013): 335–44. http://dx.doi.org/10.1139/tcsme-2013-0023.

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Springback will occur when the external force is removed after bending process in sheet metal forming. This paper proposed an adaptive-network-based fuzzy inference system (ANFIS) model for prediction the springback angle of the SPCC material after U-bending. Three parameters were selected as the main factors of affecting the springback after bending, including the die clearance, the punch radius, and the die radius. The training data were obtained from results of U-bending experiment. The training data with four different membership functions – triangular, trapezoidal, bell, and Gaussian func

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Sekhi, Ihab, Szilveszter Kovács, and Károly Nehéz. "Enhancing Decision-making in Uncertain Domains through Optimized Fuzzy Logic Systems." Periodica Polytechnica Electrical Engineering and Computer Science 69, no. 1 (2025): 63–78. https://doi.org/10.3311/ppee.38729.

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Fuzzy logic helps manage human-like reasoning in system control, mainly when traditional analysis does not work due to complex control processes. Despite its usefulness, fuzzy logic faces challenges in decision-making, especially in complex business situations and when combined with expert systems. It struggles with uncertainty and relies on various beliefs and assumptions, which is limiting compared to other methods for handling uncertainty. However, fuzzy logic can improve traditional control systems by adding a layer of intelligence. This study adapts mathematical functions like the straigh

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Yang, Liuhui, and Xiuying Wu. "A Fuzzy Neural Network-Based System for Alleviating Students’ Boredom in English Learning." Mobile Information Systems 2022 (May 25, 2022): 1–11. http://dx.doi.org/10.1155/2022/2114882.

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In order to explore students’ boredom in English learning, a recognition algorithm based on fuzzy neural network is proposed. The algorithm selects Gaussian membership function and initializes the clustering center obtained by fuzzy c-means algorithm to the center of Gaussian function, and the width of Gaussian function is obtained by the membership and center of fuzzy c-means clustering algorithm. In the construction of base classifier, diversity strategy is adopted to increase its diversity and complementarity. In the selection of base classifiers, the combination of contour coefficient and

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Apoorv Kumar. "Analysis of the Impact of Logistic Map with Gaussian Membership Function for Cryptography." Journal of Electrical Systems 20, no. 11s (2024): 87–98. https://doi.org/10.52783/jes.7079.

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This paper deals with the integration of the as logistic map with the different fuzzy number generators, resulting in a hybrid approach of encryption that can leverage the strengths and randomness of both chaotic dynamics and fuzzy logic. The chaotic maps are influenced by the Gaussian fuzzification, which produces bifurcation diagrams that have the fundamental structure of the system’s dynamics. By analyzing the chaotic behavior of the various maps and their impact on generating the pseudo-random sequences which can be suitable for the encryption, the fuzzy numbers are added to modify the cha

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Feng, Shuang, Hongxing Li, and Dan Hu. "A new training algorithm for HHFNN based on Gaussian membership function for approximation." Neurocomputing 72, no. 7-9 (2009): 1631–38. http://dx.doi.org/10.1016/j.neucom.2008.08.013.

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P, Nageswari, Bhuvaneswari M, and Veena K. "DESPECKLING OF MEDICAL IMAGES BASED ON GAUSSIAN MEMBERSHIP FUNCTION AND GRUNWALD-LETNIKNOV DERIVATIVE." International Journal of Engineering Applied Sciences and Technology 7, no. 10 (2023): 159–65. http://dx.doi.org/10.33564/ijeast.2023.v07i10.023.

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Noise removal of Ultrasound (US), Computerized Tomography (CT) and Magnetic Resonance (MR) images are really challenging task in medical point of view. As the nature of the tumor, it can present any part of these images with any shape, size and contrast that creates the de-noising process further complicated. To overcome these issues, the proposed work presents an efficient Filter for image classification and de-noising based on Gaussian Membership Function and GrunwaldDerivative. The performance of the proposed filtering method is compared with Two Step Algorithm (TSA) which classifies the im

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Gorbiychuk, Mykhail, Dmytro Kropyvnytskyi, and Vitalia Kropyvnytska. "Improving empirical models of complex technological objects under conditions of uncertainty." Eastern-European Journal of Enterprise Technologies 2, no. 2 (122) (2023): 53–63. http://dx.doi.org/10.15587/1729-4061.2023.276586.

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This paper proposes a method for improving empirical models of complex technological objects with insufficient information about the input and output values of an object's parameters. It has been established that most methods for constructing empirical models require knowledge of the statistical characteristics of the input and output values of an object. When modeling complex non-reproducible stochastic processes that evolve over time, information about the parameters and structure of an object is usually not available. A method has been proposed where input and output values are treated as f

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