Identification of Community Structure by Fuzzy clustering Theory

For multi-target tracking system, aiming at solving the problem of low precision of state estimation caused by the data correlation ambiguity, the paper presents a novel multi-sensor multi-target adaptive tracking algorithm based on fuzzy clustering theory. Based on the joint probability data association algorithm, the new approach takes account of the case that whether the measure is validated and its possibility of belong to false alarm, and improves the correlation criterion of effective measurement with existing track on the basis of fuzzy clustering theory, which all perfect the update equation of target state estimation and the covariance. Meanwhile, with the adaptive distributed fusion processing structure, it enhance the robustness of the system and without prejudice to the real-time tracking. With the simulation case studies of radar/infrared sensor fusion multi-target tracking system, it verifies the effectiveness of the proposed approach.

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The application of fuzzy clustering in the analysis of community structure

2010 Chinese Control and Decision Conference ◽

10.1109/ccdc.2010.5498569 ◽

2010 ◽

Author(s):

Aili Fang ◽

Siying Zhang

Keyword(s):

Community Structure ◽

Fuzzy Clustering

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Blind wavelet-based watermarking scheme using fuzzy clustering theory

International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003 ◽

10.1109/icnnsp.2003.1281165 ◽

2003 ◽

Cited By ~ 1

Author(s):

Jianzhen Wu ◽

Jianying Xie

Keyword(s):

Fuzzy Clustering ◽

Fuzzy Clustering Theory ◽

Watermarking Scheme

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A new method of diagnosing in-cylinder combustion deficiency based on fuzzy clustering theory and intra-cycle speed fluctuation

2010 International Conference on Artificial Intelligence and Education (ICAIE) ◽

10.1109/icaie.2010.5641030 ◽

2010 ◽

Author(s):

Chen Hongyan ◽

Yan Jin ◽

Cheng Min

Keyword(s):

Fuzzy Clustering ◽

New Method ◽

Speed Fluctuation ◽

Fuzzy Clustering Theory

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Application of fuzzy cluster analysis to Lake Simcoe crustacean zooplankton community structure

Canadian Journal of Fisheries and Aquatic Sciences ◽

10.1139/f00-231 ◽

2001 ◽

Vol 58 (2) ◽

pp. 231-240 ◽

Cited By ~ 6

Author(s):

Kenneth H Nicholls ◽

Claudiu Tudorancea

Keyword(s):

Community Structure ◽

Fuzzy Clustering ◽

Zooplankton Community ◽

Fuzzy Cluster ◽

Crustacean Zooplankton ◽

Sampling Station ◽

Ecological Data ◽

Zooplankton Community Structure ◽

Cluster Membership ◽

Lake Simcoe

Fuzzy clustering generates cluster membership weights that indicate how tightly each object is linked to its cluster relative to other clusters of a dendrogram. In a fuzzy clustering of the crustacean-zooplankton taxa of Lake Simcoe, a large (720 km2) hardwater lake in Ontario, Canada, we show how the membership weights can be used to rank all taxa for their contribution to the sampling unit (SU) classification, where the total number of SUs was 84 (7 years × 12 sampling sites). The validity of the results was confirmed by comparison with other more traditional methods of identifying variables important for object classifications and by permutation tests of matrix correlation before and after removal of low-ranked and highly ranked species. Fuzzy clustering of Lake Simcoe SUs also revealed (i) the likelihood of trends in zooplankton community composition over the 7-year period and (ii) differences in composition possibly related to sampling-station depth. In particular, the shallowest sampling station in southern Cook's Bay had a zooplankton community structure that differed significantly from other stations during all years of the study. As a preliminary screening or data exploration tool, fuzzy clustering is particularly useful for analysis of ecological data.

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Binarization Segmentation Based on Fuzzy Clustering Theory

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.740.584 ◽

2015 ◽

Vol 740 ◽

pp. 584-587

Author(s):

Hong Jun Wang ◽

Hao Song ◽

Hui Zhao ◽

You Jun Yue

Keyword(s):

Fuzzy Clustering ◽

Clustering Algorithm ◽

Color Image ◽

Operation Time ◽

Feature Space ◽

Segmentation Method ◽

Low Snr ◽

Segmentation Accuracy ◽

Plant Image ◽

Fuzzy Clustering Theory

Aiming at the problem of color image two value segmentation, present an improved two value plant image segmentation method based on fuzzy clustering theory. In the light of the problem that fuzzy clustering algorithm is difficult to segment the low SNR image, improve Membership function; Aiming at the problem that image pixel information is too large, mapping image information to the gray feature space, treat the same gray pixel as a whole, improve operation efficiency. The experimental results show that, the improved algorithm has stronger anti noise ability, higher segmentation accuracy, shorter operation time, has good prospects for engineering applications.

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