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A fuzzy clustering neural networks for motion equations of synchro-drive robot

EXPERT SYSTEMS WITH APPLICATIONS 2010年第12期37卷 7819-7824页

作者： Aydin, Serkan Celal Bayar Univ TMYO Ind Elect Programme TR-45400 Turgutlu Manisa Turkey

Motion equations for synchro-drive robot Nomad 200 are solved by using fuzzy clustering neural networks. The trajectories of the Nomad 200 are assumed to be composed of line segments and curves. The structure of the curves is determined by only two parameters (turn angle and translational velocity in the curve). The curves of the trajectories are found by using artificial neural networks (ANN) and fuzzy C-means clustered (fcm) ANN. In this study a clustering method is used in order to improve the learning and the test performance of the ANN. The fcm algorithm is successfully used in clustering ANN datasets. Thus, the best of training dataset of ANN is achieved and minimum error values are obtained. It is seen that, fcm-ANN models are better than the classic ANN models. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Synchro-drive robot Neural networks fcm algorithm Clustering

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A Novel Rough Neural Network Based on Fuzzy Partition

A Novel Rough Neural Network Based on Fuzzy Partition

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22nd Chinese Control and Decision Conference

作者： Xu Xiang Zhang Dongbo Wang Yaonanr Liu Ziwen Xiangtan Univ Coll Informat Engn Xiangtan 411105 Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China

ISBN: (纸本)9781424451821

A method to partition the universe of discourse based on fuzzy clustering is proposed to solve the partition problem in the process of constructing rough neural network. Considering traditional clustering algorithm has the problem of easily fall into local optimum, a modified PSO algorithm with crossover and mutation operators is combined with fcm algorithm. And a new fuzzy clustering algorithm (CMPSO-fcm) is proposed. The searching capability and clustering effectiveness are improved by this new algorithm. Then the fuzzy similar matrix, which is used for attribute reduction, is calculated by using fuzzy partition matrix and the definition of fuzzy similar measure after fuzzy clustering result is achieved. And a set of fuzzy rough decision rules arc acquired by entropy method. Finally, a rough neural network is designed under these decision rules. Experiments results show that, compared with traditional rough neural network, this method has superiorities at the aspect of structure, classification precision and generalization.

关键词： rough neural network fuzzy clustering PSO algorithm fcm algorithm entropy attribute reduction

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Color Quantization with Clustering By F-PSO-GA

Color Quantization with Clustering By F-PSO-GA

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2010 IEEE International Conference on Intelligent Computing and Intelligent Systems(2010 IEEE 智能计算与智能系统国际会议 ICIS 2010)

作者： Fatemh Alamdar Zahra bahmani Saman Haratizadeh Faculty of Engineering and Technology Alzahra University Tehran Iran Department of Computer Engineering Sharif University Tehran Iran

Color quantization is a technique for processing and reduction colors in image. The purposes of color quantization are displaying images on limited hardware, reduction use of storage media and accelerating image sending time. In this paper a hybrid algorithm of CA and Particle Swarm Optimization algorithms with fcm algorithm is proposed. Finally, some of color quantization algorithms are reviewed and compared with proposed algorithm. The results demonstrate Superior performance of proposed algorithm in comparison with other color quantization algorithms.

关键词： Clustering Color quantization fcm algorithm Genetic algorithm Particle Swarm Optimization(PSO)algorithm

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Parallel Approach to Fuzzy Vector Quantization for Image Compression

Parallel Approach to Fuzzy Vector Quantization for Image Com...

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3rd International Workshop on e-Activity (IWEA2009)/10th ACIS International Conference on Software Engineering Artificial Intelligence, Networking and Parallel/Distributed Computing

作者： Van Luong, Huynh Kim, Yong-Min Kim, Byung-Kook Kim, Jong-Myon Kim, Cheol-Hong Univ Ulsan Sch Comp Engn & Informat Technol Ulsan 680749 South Korea Chonnam Natl Univ Sch Elect & Comp Engn Gwangju South Korea

ISBN: (纸本)9780769536422

Fuzzy clustering based vector quantization algorithm has been widely used in the field of data compression since the use of fuzzy, clustering analysis in the early stages of a vector quantization process can make this process less sensitive to initialization. However, the process of fuzzy clustering is computationally very intensive because of its complex framework for the quantitative formulation of the uncertainty involved in the training vector space. To overcome the computational burden of the process, we introduce a parallel implementation of Fuzzy Vector Quantization (FVQ) using a representative data parallel architecture which consists of 4,096 processing elements (PEs). Our parallel approach provides a computationally efficient solution with the 4,096 PEs by employing an effective vector assignment strategy for the transition from soft to crisp decisions during the clustering process. Experimental results show that our parallel approach provides 1000x greater performance and 100x higher energy efficiency than other implementations using commercial processors such as ARM families.

关键词： Fuzzy Vector Quantization parallel processing data parallel architecture fcm algorithm codebook

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Document Image Binarization Based on Nfcm

Document Image Binarization Based on NFCM

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2nd International Congress on Image and Signal Processing

作者： Tong Li-Jing Chen Kan Zhang Yan Fu Xiao-Ling Duan Jian-Yong N China Univ Technol Multimedia Technol Lab Beijing Peoples R China

ISBN: (纸本)9781424441303

Document image binarization plays an important role in image segmentation and its effect directly impacts on the quality of the OCR recognition system. However, binarization is difficult for camera-based document images with poor contrast or illumination. In this paper, we propose a binarization algorithm, called Nfcm, for camera-based document image. Nfcm, a local threshold method, is a combination of Niblack algorithm and fcm (Fuzzy C-Means) algorithm. It is good at not only preserving the character stokes, but also alleviating the ghost artifacts. Comparative experiments show that Nfcm can obtain favorable results with respect to the OCR performance.

关键词： binarization document image threshold fcm algorithm

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Improving probabilities in a fuzzy clustering partition

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FUZZY SETS AND SYSTEMS 2008年第4期159卷 406-421页

作者： Soto, J. Flores-Sintas, A. Palarea-Albaladejo, J. Univ Catolica San Antonio Dept Comp Sci Murcia 30107 Spain

Fuzzy clustering procedures based on the fcm algorithm calculate group membership probabilities or degrees taking into account the distance between objects and group prototypes. This paper seeks to improve the computation of such membership probabilities by a new membership function which also reflects the relative position of an object with respect to each group. By this way, some illogical results are avoided and a convex partition is provided. Finally, numerical examples illustrate the performance of the proposed algorithm. (c) 2007 Published by Elsevier B.V.

关键词： fuzzy clustering fcm algorithm membership probabilities convex partitions

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Remote Sensing Image Classification Based on Improved Fuzzy c-Means

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Geo-Spatial Information Science 2008年第2期11卷 90-94页

作者： YU Jie GUO Peihuang CHEN Pinxiang ZHANG Zhongshan RUAN Wenbin School of Remote Sensing and Information Engineering Wuhan University 129 Luoya Road Wuhan 430079 China 不详

Classification is always the key point in the field of remote sensing. Fuzzy c-Means is a traditional clustering algorithm that has been widely used in fuzzy clustering. However, this algorithm usually has some weaknesses, such as the problems of falling into a local minimum, and it needs much time to accomplish the classification for a large number of data. In order to overcome these shortcomings and increase the classifi-cation accuracy, Gustafson-Kessel (GK) and Gath-Geva (GG) algorithms are proposed to improve the tradi-tional fcm algorithm which adopts Euclidean distance norm in this paper. The experimental result shows that these two methods are able to detect clusters of varying shapes, sizes and densities which fcm cannot do. Moreover, they can improve the classification accuracy of remote sensing images.

关键词： fcm algorithm GK algorithm GG algorithm remote sensing classification

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Fuzzy logic based segmentation of microcalcification in breast using digital mammograms considering multiresolution

Fuzzy logic based segmentation of microcalcification in brea...

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International Machine Vision and Image Processing Conference

作者： Bhattacharya, Mahua Das, Arpita Indian Inst Informat Technol & Management Gwalior Morena Link Rd Gwalior 474003 India Univ Calcutta Inst Radiophys & Elect Kolkata 700009 W Bengal India

ISBN: (纸本)9780769528878

Breast cancer is one of the leading causes of death for women. Small clusters of microcalcifications appearing as collection of white spots on mammograms show an early warning of breast cancer. In present paper a novel approach of segmentation implemented on X-ray mammograms for more accurate detection of microcalcification clusters has been introduced. The method is based on discrete wavelet transform due to its multiresolution properties. Morphological tophat algorithm is applied for contrast enhancement of the calcification clusters. Finally fuzzy c-means clustering (fcm) algorithm has been implemented for intensity-based segmentation. The proposed technique is compared with conventional global thresholding method and experimental results show the good properties of the proposed technique.

关键词： microcalcifications wavelet transform morphological top-hat transformation fcm algorithm

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Biomedical Image Processing Using fcm algorithm Based on the Wavelet Transform

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Journal of Wuhan University of Technology(Materials Science) 2004年第3期19卷 18-20页

作者：闫玉华 Wuhan University of Technology

An effective processing method for biomedical images and the Fuzzy C-mean (fcm) algorithm based on the wavelet transform are *** using hierarchical wavelet decomposition, an original image could be decomposed into one lower image and several detail images. The segmentation started at the lowest resolution with the fcm clustering algorithm and the texture feature extracted from various sub-bands. With the improvement of the fcm algorithm, fcm alternation frequency was decreased and the accuracy of segmentation was advanced.

关键词： biomedical image processing fcm algorithm wavelet transform texture feature

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Fuzzy C-means clustering algorithm based on incomplete data

Fuzzy C-means clustering algorithm based on incomplete data

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IEEE International Conference on Information Acquisition

作者： Jia, Zhiping Yu, Zhiqiang Zhang, Chenghui Shandong Univ Dept Comp Sci & Technol Jinan 250100 Shandong Peoples R China

ISBN: (纸本)1424405289

In order to solve the problem that the traditional fuzzy c-means(fcm) clustering algorithm can not directly act on incomplete data, a modified algorithm IDfcm(Incomplete Data fcm) based on the fcm algorithm is proposed. The IDfcm algorithm takes the percentage of incomplete data in datasets and its effect on clustering analysis into consideration. Finally, the experimental clustering results of IRIS data and mobile distributed inspected data of the ocean are given, which can clearly prove that the IDfcm algorithm is very efficient for clustering incomplete data.

关键词： IDfcm algorithm fcm algorithm fuzzy clustering incomplete datum

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