Augmentation of the reconstruction performance of Fuzzy C-Means with an optimized fuzzification factor vector

Xu, Kaijie; Pedrycz, Witold; Li, Zhiwu

doi:10.1016/j.knosys.2021.106951

Cited by 8 publications

(4 citation statements)

References 33 publications

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“…Te decoding mechanism primarily involves reconstructing numeric data based on the generated prototypes and the membership matrix, which constitutes the formed information granules. Te form of the decoding expression can be obtained by minimizing the following cost function [28][29][30][31]:…”

Section: Decoding Structuredmentioning

confidence: 99%

Constructing Perturbation Matrices of Prototypes for Enhancing the Performance of Fuzzy Decoding Mechanism

Xu,

Liu

et al. 2024

International Journal of Intelligent Systems

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Granular computing (GrC) embraces a spectrum of concepts, methodologies, methods, and applications, which dwells upon information granules and their processing. Fuzzy C-means (FCM) based encoding and decoding (granulation-degranulation) mechanism plays a visible role in granular computing. Fuzzy decoding mechanism, also known as the reconstruction (degranulation) problem, has become an intensively studied category in recent years. This study mainly focuses on the improvement of the fuzzy decoding mechanism, and an augmented version achieved through constructing perturbation matrices of prototypes is put forward. Particle swarm optimization is employed to determine a group of optimal perturbation matrices to optimize the prototype matrix and obtain an optimal partition matrix. A series of experiments are carried out to show the enhancement of the proposed method. The experimental results are consistent with the theoretical analysis and demonstrate that the developed method outperforms the traditional FCM-based decoding mechanism.

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Section: Decoding Structuredmentioning

confidence: 99%

Constructing Perturbation Matrices of Prototypes for Enhancing the Performance of Fuzzy Decoding Mechanism

Xu,

Liu

et al. 2024

International Journal of Intelligent Systems

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“…When λ takes λ max , the equation group can be obtained. The weight of each index factor can be determined by solving the solution vector of the equation group [24,25], which is W = ðw 1 , w 2 ,⋯,w n Þ…”

Section: Weight Determination Of Classroom Teaching Quality Evaluatio...mentioning

confidence: 99%

Piano Information Teaching Mode Based on Deep Learning Algorithm

Wang

2022

Wireless Communications and Mobile Computing

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In order to improve the effect of piano information teaching, a piano information teaching mode based on deep learning algorithm is proposed. The teaching objectives are divided into three levels: classroom teaching objectives, curriculum objectives, and education and training objectives. A piano information classroom integrating cloud application platform, teaching platform, resource platform, learning space, and interactive classroom is built. The previous teaching mode is optimized to build an innovative teaching mode of piano information classroom. The evaluation index system of piano informatization classroom teaching quality is constructed, and the hierarchical structure model of each evaluation index is established by using the analytic hierarchy process. The hierarchical analysis method is used to establish a hierarchical structure model of each evaluation index. The judgment matrix is determined by the nine-digit scale method. After the consistency verification of the judgment matrix, the weight of the quality evaluation of piano information classroom teaching is calculated. The new mode optimizes the weight and threshold of BP neural network in deep learning algorithm by genetic algorithm (GA). The weight of each classroom teaching quality evaluation index is input into the GA-BP neural network, and the network output result is the piano information classroom teaching quality evaluation score. The test results show that the optimal number of hidden layer nodes for the BP neural network is 7, when the GA-BP neural network iterations are 95. This method can evaluate the quality of piano information classroom teaching, with high evaluation accuracy and strong practical application.

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“…According to this method, calculate circularly. When J reaches the minimum value, stop the calculation and the values of c i and u ij are output to complete the clustering [30]- [32].…”

Section: B Fuzzy C-means Clusteringmentioning

confidence: 99%

A Novel Rolling Bearing Fault Diagnosis Method Based on Adaptive Feature Selection and Clustering

Hou

Ahmad

et al. 2021

IEEE Access

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Rolling bearing is an important part of mechanical equipment. Timely detection of rolling bearing fault is one of the important factors to ensure the safe operation of equipment. In order to diagnose rolling bearing fault accurately, a novel rolling bearing fault diagnosis method based on adaptive feature selection and clustering is proposed. Firstly, the vibration signal obtained from rolling bearing is decomposed by ensemble empirical mode decomposition(EEMD) to extract as much important information as possible. Feature extraction is performed for each intrinsic mode function(IMF) component and the original signal, and finally 240 features are obtained. And the Chi-square Test algorithm, Variance-Relief-F algorithm, and hierarchical clustering algorithm are used to filter all the features in layers to obtain the optimal features. Then the optimal features are input into fuzzy c-means(FCM) clustering to complete fault diagnosis. After the fault diagnosis analysis of four groups of vibration signal data, it is found that whether the characteristic number parameters are set based on engineering experience or adaptive feature selection, good fault diagnosis results are obtained. Furthermore, through comparative experiments, the fault diagnosis effect of the method based on adaptive parameter setting is better. The results indicate that the proposed adaptive parameter fault diagnosis method is feasible and effective for rolling bearing fault diagnosis.INDEX TERMS Fault diagnosis, Rolling bearing, Adaptive feature selection, Relief-F Algorithm, Clustering.

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Augmentation of the reconstruction performance of Fuzzy C-Means with an optimized fuzzification factor vector

Cited by 8 publications

References 33 publications

Constructing Perturbation Matrices of Prototypes for Enhancing the Performance of Fuzzy Decoding Mechanism

Constructing Perturbation Matrices of Prototypes for Enhancing the Performance of Fuzzy Decoding Mechanism

Piano Information Teaching Mode Based on Deep Learning Algorithm

A Novel Rolling Bearing Fault Diagnosis Method Based on Adaptive Feature Selection and Clustering

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