A new extension of fuzzy C-Means algorithm using non Euclidean distance and kernel methods

Mohamed, Bouzbida; Ahmed, Troudi; Houcine, Lassad; Châari, Abdelkader

doi:10.1109/codit.2013.6689551

Cited by 2 publications

(3 citation statements)

References 12 publications

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“…The kernel function is defined as a generalization of the distance metric that measures the distance between two data points mapped into a future space in which the data are more clearly separable [12,[19][20][21].…”

Section: Proposed Robust Kernel Possibilistic Fuzzy -Meansmentioning

confidence: 99%

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Robust Kernel Clustering Algorithm for Nonlinear System Identification

Mohamed

Houcine

Châari

2017

Mathematical Problems in Engineering

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In engineering field, it is necessary to know the model of the real nonlinear systems to ensure its control and supervision; in this context, fuzzy modeling and especially the Takagi-Sugeno fuzzy model has drawn the attention of several researchers in recent decades owing to their potential to approximate nonlinear behavior. To identify the parameters of Takagi-Sugeno fuzzy model several clustering algorithms are developed such as the Fuzzy C-Means (FCM) algorithm, Possibilistic C-Means (PCM) algorithm, and Possibilistic Fuzzy C-Means (PFCM) algorithm. This paper presents a new clustering algorithm for Takagi-Sugeno fuzzy model identification. Our proposed algorithm called Robust Kernel Possibilistic Fuzzy C-Means (RKPFCM) algorithm is an extension of the PFCM algorithm based on kernel method, where the Euclidean distance used the robust hyper tangent kernel function. The proposed algorithm can solve the nonlinear separable problems found by FCM, PCM, and PFCM algorithms. Then an optimization method using the Particle Swarm Optimization (PSO) method combined with the RKPFCM algorithm is presented to overcome the convergence to a local minimum of the objective function. Finally, validation results of examples are given to demonstrate the effectiveness, practicality, and robustness of our proposed algorithm in stochastic environment.

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Section: Proposed Robust Kernel Possibilistic Fuzzy -Meansmentioning

confidence: 99%

“…Define a nonlinear map as Φ : → Φ( ) ∈ , where ∈ , and is the transformed feature space with higher or even infinite dimension. denotes the data space mapped into [20][21][22].…”

Section: Proposed Robust Kernel Possibilistic Fuzzy -Meansmentioning

confidence: 99%

Robust Kernel Clustering Algorithm for Nonlinear System Identification

Mohamed

Houcine

Châari

2017

Mathematical Problems in Engineering

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“…This research combined the measurement data from the sensor and clustering data for prediction. Neurofuzzy c-means clustering [17], which applies the Euclidean distance to the clustering technique, was utilized in many applications such as brain tumor in MRI images [18], remote sensing images [19], data clustering with image segmentation [20], and processing time improvement without performance effect [21]. In [22], branch and bound (BNB) was used for cyclic scheduling of timed Petri nets (TPN) based on the manufacturing systems.…”

Section: Introductionmentioning

confidence: 99%

Neurofuzzy c-Means Network-Based SCARA Robot for Head Gimbal Assembly (HGA) Circuit Inspection

Kiatwanidvilai

Praserttaweelap

2018

Computational Intelligence and Neuroscience

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Decision and control of SCARA robot in HGA (head gimbal assembly) inspection line is a very challenge issue in hard disk drive (HDD) manufacturing. The HGA circuit called slider FOS is a part of HDD which is used for reading and writing data inside the disk with a very small dimension, i.e., 45 × 64 µm. Accuracy plays an important role in this inspection, and classification of defects is very crucial to assign the action of the SCARA robot. The robot can move the inspected parts into the corresponding boxes, which are divided into 5 groups and those are “Good,” “Bridging,” “Missing,” “Burn,” and “No connection.” A general image processing technique, blob analysis, in conjunction with neurofuzzy c-means (NFC) clustering with branch and bound (BNB) technique to find the best structure in all possible candidates was proposed to increase the performance of the entire robotics system. The results from two clustering techniques which are K-means, Kohonen network, and neurofuzzy c-means were investigated to show the effectiveness of the proposed algorithm. Training results from the 30x microscope inspection with 300 samples show that the best accuracy for clustering is 99.67% achieved from the NFC clustering with the following features: area, moment of inertia, and perimeter, and the testing results show 92.21% accuracy for the conventional Kohonen network. The results exhibit the improvement on the clustering when the neural network was applied. This application is one of the progresses in neurorobotics in industrial applications. This system has been implemented successfully in the HDD production line at Seagate Technology (Thailand) Co. Ltd.

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A new extension of fuzzy C-Means algorithm using non Euclidean distance and kernel methods

Cited by 2 publications

References 12 publications

Robust Kernel Clustering Algorithm for Nonlinear System Identification

Robust Kernel Clustering Algorithm for Nonlinear System Identification

Neurofuzzy c-Means Network-Based SCARA Robot for Head Gimbal Assembly (HGA) Circuit Inspection

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