Classification of Ship’ Magnetic Field and Feature Selection Based on the Improved Weighted Fuzzy Clustering Algorithm

Magnetic field data of ship has three-component,and traditional weighted fuzzy clustering algorithm(FCA) can’t deal with the three-component data. We improve the traditional FCA by changing the objective function and added weights calculation of three-component of magnetic field in the function.Give the equation to compute the weights of three-component.Put forward new steps for improved algorithm.Use ships’ data to test the improved algorithm and giving the conclusion.

Download Full-text

A modified fuzzy clustering algorithm for operator independent brain tissue classification of dual echo MR images

Magnetic Resonance Imaging ◽

10.1016/s0730-725x(99)00055-7 ◽

1999 ◽

Vol 17 (7) ◽

pp. 1065-1076 ◽

Cited By ~ 119

Author(s):

J. Suckling ◽

T. Sigmundsson ◽

K. Greenwood ◽

E.T. Bullmore

Keyword(s):

Brain Tissue ◽

Fuzzy Clustering ◽

Clustering Algorithm ◽

Mr Images ◽

Tissue Classification ◽

Fuzzy Clustering Algorithm ◽

Dual Echo

Download Full-text

A Novel Intuitionistic Fuzzy Clustering Algorithm Based on Feature Selection for Multiple Object Tracking

International Journal of Fuzzy Systems ◽

10.1007/s40815-019-00645-7 ◽

2019 ◽

Vol 21 (5) ◽

pp. 1613-1628 ◽

Cited By ~ 1

Author(s):

Liang-qun Li ◽

Xiao-li Wang ◽

Zong-xiang Liu ◽

Wei-xin Xie

Keyword(s):

Feature Selection ◽

Object Tracking ◽

Fuzzy Clustering ◽

Clustering Algorithm ◽

Multiple Object Tracking ◽

Intuitionistic Fuzzy ◽

Fuzzy Clustering Algorithm ◽

Multiple Object ◽

Selection For ◽

Intuitionistic Fuzzy Clustering

Download Full-text

TS fuzzy model identification by a novel objective function based fuzzy clustering algorithm

2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL) ◽

10.1109/ciel.2014.7015742 ◽

2014 ◽

Cited By ~ 3

Author(s):

Tanmoy Dam ◽

Alok Deb

Keyword(s):

Objective Function ◽

Fuzzy Clustering ◽

Clustering Algorithm ◽

Model Identification ◽

Fuzzy Model ◽

Ts Fuzzy Model ◽

Fuzzy Clustering Algorithm

Download Full-text

A FUZZY CLUSTERING ALGORITHM FOR THE CLASSIFICATION OF NON-LINEAR FUNCTIONAL BEHAVIORS IN NUCLEAR REPROCESSING PLANTS

Computational Intelligence in Decision and Control ◽

10.1142/9789812799470_0068 ◽

2008 ◽

Author(s):

P. BARALDI ◽

F. CADINI ◽

E. ZIO ◽

I. C. POPESCU ◽

P. RICHIR ◽

...

Keyword(s):

Fuzzy Clustering ◽

Linear Functional ◽

Clustering Algorithm ◽

Fuzzy Clustering Algorithm ◽

Nuclear Reprocessing ◽

Non Linear

Download Full-text

A robust fuzzy clustering algorithm for the classification of remote sensing images

IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120) ◽

10.1109/igarss.2000.858335 ◽

2002 ◽

Cited By ~ 1

Author(s):

H. Barni ◽

A. Garzelli ◽

A. Mecocci ◽

L. Sabatini

Keyword(s):

Remote Sensing ◽

Fuzzy Clustering ◽

Clustering Algorithm ◽

Remote Sensing Images ◽

Fuzzy Clustering Algorithm

Download Full-text

Fuzzy clustering algorithm-based classification of daily electrical load patterns

2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) ◽

10.1109/fskd.2015.7381913 ◽

2015 ◽

Cited By ~ 1

Author(s):

Yi Sun ◽

Wei Gu ◽

Jun Lu ◽

Zenghui Yang

Keyword(s):

Fuzzy Clustering ◽

Clustering Algorithm ◽

Electrical Load ◽

Fuzzy Clustering Algorithm

Download Full-text

Pairwise constraints cross entropy fuzzy clustering algorithm based on manifold learning and feature selection

Journal of Physics Conference Series ◽

10.1088/1742-6596/1948/1/012033 ◽

2021 ◽

Vol 1948 (1) ◽

pp. 012033

Author(s):

Wei Cai ◽

Shengbing Xu ◽

LiangJun Zhang ◽

Jiongzhi Liu ◽

Peixuan Chen

Keyword(s):

Feature Selection ◽

Manifold Learning ◽

Fuzzy Clustering ◽

Clustering Algorithm ◽

Cross Entropy ◽

Pairwise Constraints ◽

Fuzzy Clustering Algorithm

Download Full-text

FUZZY CLUSTERING ALGORITHM FOR INTEGRATING MULTISCALE SPATIAL CONTEXT IN IMAGE SEGMENTATION BY HIDDEN MARKOV RANDOM FIELD MODELS

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001413550057 ◽

2013 ◽

Vol 27 (03) ◽

pp. 1355005 ◽

Cited By ~ 4

Author(s):

GUO-YING LIU ◽

AI-MIN WANG

Keyword(s):

Random Field ◽

Objective Function ◽

Fuzzy Clustering ◽

Markov Random Field ◽

Clustering Algorithm ◽

Hidden Markov ◽

Wavelet Coefficient ◽

Fuzzy Clustering Algorithm ◽

Markov Random ◽

Hidden Markov Random Field

In this study, a fuzzy clustering algorithm, MRHMRF-FCM, is proposed to capture and utilize the multiscale spatial constrains by employing multiresolution representations for the label image and the observed image in wavelet domain. In this algorithm, the inner-scale and inter-scale spatial constrains, respectively modeled by the hidden Markov random field models, serve as the penalization terms for the objective function of the FCM algorithm. On each scale, the improved objective function is optimized by taking advantage of Lagrange multipliers, and the final label of wavelet coefficient is determined by iteratively updating the membership degree and cluster centers. The experimental results on synthetic images, natural scenery color images and remote sensed images show that the proposed algorithm obtains much better segmentation results, such as accurately differentiating different regions and being immune to noise.

Download Full-text