检索结果-内蒙古大学图书馆

fuzzy Neural Network Blind Equalization algorithm Based on Signal Transformation

International conference on Frontiers of Manufacturing and Design Science (IcFMD2010)

作者： Guo, Yecai Liu, Zhengxin Nanjing Univ Informat Sci & Technol Coll Elect & Informat Engn Nanjing 210044 Jiangsu Peoples R China Anhui Univ Sci & Technol Sch Elect Engn & Informat Huainan 232001 Peoples R China

ISBN: (纸本)9783037850046

To recover QAM signals at the receiver of blind equalizer, a fuzzy c-means clustering Neural Network Blind Equalization algorithm based on Signal Transformation (ST-FNN-BEA) is proposed. The proposed algorithm uses signal transformation method to debase the computational complexity of equalizer input signals and speed up the convergence rate, and makes use of fuzzy c-means clustering algorithm dividing the equalizer input signals into each cluster center with different membership values to improve the equalization performance. The proposed ST-FNN-BEA outperforms Neural Network Blind Equalization algorithm (NN-BEA) and Neural Network Blind Equalization algorithm based on Signal Transformation (ST-NN-BEA) in improving convergence rates and reducing mean square error. The performance of ST-FNN-BEA is proved by the computer simulation with underwater acoustic channels.

关键词： signal transformation fuzzy c-means clustering algorithm fuzzy neural network underwater acoustic channel

来源：评论

学校读者我要写书评

暂无评论

Sample Selection Method of Neural Network Based on FcM and NN method

Sample Selection Method of Neural Network Based on FCM and N...

引用

2011 International conference on computers, communications, control and Automation

作者： Zhenning Zhao Su Ling Qinfeng Zhang North China Electric Power Research Institute Co. Ltd North China Electric Power University

In the application of neural network, we often encounter in such problems as the sample set contains too many similar samples or sample features are not representative *** these problems may lead to "over learning" phenomenon or the case that the results predicted by neural network model deviate largely from the actual results. The paper proposes a method of using fuzzy c-means(FcM) clustering algorithm and the nearest neighbor(NN) method to establish a neural network sample set. This method can achieve the purpose of establishing the best neural network model with a small and representative sample set, which is a good guide for the application of neural network.

关键词： fuzzy c-means clustering algorithm The nearest neighbor method Sample Selection

来源：评论

学校读者我要写书评

暂无评论

SAMPLE SELEcTION METHOD OF NEURAL NETWORK BASED ON FcM AND NN METHOD

SAMPLE SELECTION METHOD OF NEURAL NETWORK BASED ON FCM AND N...

引用

The 2011 International conference on Mechanical Engineering and Technology

作者： Zhenning Zhao Su Ling Qinfeng Zhang North China Electric Power Research Institute Co.Ltd North China Electric Power University

In the application of neural network, we often encounter in such problems as the sample set contains too many similar samples or sample features are not representative enough. All these problems may lead to "over learning" phenomenon or the case that the results predicted by neural network model deviate largely from the actual results. The paper proposes a method of using fuzzy c-means(FcM) clustering algorithm and the nearest neighbor(NN) method to establish a neural network sample set. This method can achieve the purpose of establishing the best neural network model with a small and representative sample set, which is a good guide for the application of neural network.

关键词： fuzzy c-means clustering algorithm The nearest neighbor method Sample Selection

来源：评论

学校读者我要写书评

暂无评论

A modified fuzzy c-means with Particle Swarm Optimization adaptive image segmentation algorithm

A modified Fuzzy C-Means with Particle Swarm Optimization ad...

引用

2010 International conference on Display and Photonics

作者： Gu, Yingjie Jia, Zhenhong Yang, Jie Pang, Shaoning Xinjiang Univ Coll Informat Sci & Engn Urumqi 830046 Peoples R China Shanghai Jiao Tong Univ Inst Image Processing & Pattern Recognit Shanghai 200240 Peoples R China Univ Auckland Knowledge Engn & Res Discovery Inst Auckland New Zealand

ISBN: (纸本)9780819482402

A new method which the numbers of cluster is self-adapted and use up and down cut-off of FcM combined with PSO to take place of common FcM combined with PSO is proposed in this paper. Experiment's results show that compared with the method of combining the particle swarm optimization (PSO) with common FcM, it helps to make a better effect on image segmentation and optimize the numbers of cluster and converge the rate quickly.

关键词： image segmentation Particle Swarm Optimization fuzzy c-means clustering algorithm global optimization up and down cut-off

来源：评论

学校读者我要写书评

暂无评论

Interval competitive agglomeration clustering algorithm

引用

EXPERT SYSTEMS WITH APPLIcATIONS 2010年第9期37卷 6567-6578页

作者： Jeng, Jin-Tsong chuang, chen-chia Tao, c. W. Natl Ilan Univ Dept Elect Engn Ilan 260 Taiwan Natl Formosa Univ Dept Comp Sci & Informat Engn Huwei Township 632 Yunlin County Taiwan

In this study, an interval competitive agglomeration (IcA) clustering algorithm is proposed to overcome the problems of the unknown clusters number and the initialization of prototypes in the clustering algorithm for the symbolic interval-values data. In the proposed IcA clustering algorithm, both the Euclidean distance measure and the Hausdorff distance measure for the symbolic interval-values data are independently considered. Besides, the advantages of both hierarchical clustering algorithm and partitional clustering algorithm are also incorporated into the IcA clustering algorithm. Hence, the IcA clustering algorithm can be fast converges in a few iterations regardless of the initial number of clusters. Moreover, it is also converges to the same optimal partition regardless of its initialization. Experiments with simply data sets and real data sets show the merits and usefulness of the IcA clustering algorithm for the symbolic interval-values data. (c) 2010 Elsevier Ltd. All rights reserved.

关键词： Symbolic interval-values data Interval competitive agglomeration clustering algorithm fuzzy c-means clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

Adaptive learning of ordinal-numerical mappings through fuzzy clustering for the objects of mixed features

引用

fuzzy SETS AND SYSTEMS 2010年第4期161卷 564-577页

作者： Lee, Mahnhoon Pedrycz, Witold Thompson Rivers Univ Dept Comp Sci Kamloops BC V2C 5N3 Canada Univ Alberta Edmonton AB Canada Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland

Ordinal feature values are totally ordered labels that can be considered as fuzzy sets. The formulation of proper fuzzy sets for ordinal labels is important for the systems that deal with the objects of mixed feature types. When a proper ordinal-numerical mapping for an ordinal feature of interest is given, proper fuzzy sets for the labels of the ordinal feature can be easily formulated. In this paper. we propose an adaptive method to learn proper ordinal-numerical mappings for ordinal features of interest from a given objects of mixed features including the ordinal features. The method starts with uniform ordinal-numerical mappings, and performs two steps iteratively. The first step computes a fuzzy partition over the given object set with the ordinal-numerical mappings. The second step learns new ordinal-numerical mappings from the new fuzzy partition in the way that the new mappings make the similarity between two ordinal labels be similar to the average similarity between the objects having the two labels. respectively. Through the alternate repetition of the two steps. both of the ordinal-numerical mappings and the clustering quality become gradually improved. The validity of the proposed method is strongly supported through the experiments with a modified fuzzy c-means clustering algorithm in which the proposed method is implemented. crown copyright (c) 2009 Published by Elsevier B.V. All rights reserved.

关键词： Ordinal-numerical mapping fuzzy clustering Similarity fuzzy c-means clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

A modified fuzzy c-means with Particle Swarm Optimization adaptive image segmentation algorithm

A modified Fuzzy C-Means with Particle Swarm Optimization ad...

引用

2010 International conference on Display and Photonics

作者： Yingjie Gu a ,Zhenhong Jia a ,Jie Yang b ,Shaoning Pang c a.college of Information science&engineering,Xinjiang University,Urumqi 830046,P.R.china b.The Institute of Image Processing and Pattern Recognition,Shanghai Jiaotong University, Shanghai 200240,P.R.china c.Knowledge Engineering&Research Discovery Institute,Auckland University of Technology, Auckland,New Zealand

A new method which the numbers of cluster is self-adapted and use up and down cut-off of FcM combined with PSO to take place of common FcM combined with PSO is proposed in this ***’s results show that compared with the method of combining the particle swarm optimization(PSO)with common FcM,it helps to make a better effect on image segmentation and optimize the numbers of cluster and converge the rate quickly.

关键词： image segmentation Particle Swarm Optimization fuzzy c-means clustering algorithm global optimization up and down cut-off

来源：评论

学校读者我要写书评

暂无评论

A computer Vision-based classification Method for Pearl Quality Assessment

A Computer Vision-based Classification Method for Pearl Qual...

引用

International conference on computer Technology and Development

作者： Tian, chunyu Zaozhuang Coll Zaozhuang 277160 Shandong Peoples R China

ISBN: (纸本)9780769538921

Pearl's color is an important feature to assess its value, including the hue and its color depth. A method for pearl color classification was investigated in this paper computer Vision is used to process the pearl image after transforming it from RGB to HSV color model, which can show the hue and color depth information of pearl. According to the histogram of V (Value) weight, the bright area is extracted by Ostu Segmentation and the average value of H (Hue) and S (saturation) are obtained Aiming at the standards of hue classification, the artificial neural network method based on RPROP algorithm is adopted;Aiming at the color depth's difference, fuzzy c-means clustering algorithm is adopted to class' the average value of S. The proposed method can be used for the first classification according to the surface color of pearl and further classification according to the saturation of pearl in the same color series and realizing the standard classification of pearl quality.

关键词： color HSV fuzzy c-means clustering algorithm Artificial Neural Network

来源：评论

学校读者我要写书评

暂无评论

Application of Dynamic Tunneling fuzzy c-means algorithm in Dissolved Gas of Transformer Oil

Application of Dynamic Tunneling Fuzzy C-Means Algorithm in ...

引用

2009 9th International conference on Electronic Measurement & Instruments(第九届电子测量与仪器国际会议 IcEMI'2009)

作者： Tao chang caixin Sun Xiaoxing Zhang Hao Xiong Chongqing Electric Power Test and Research Institute Chongqing401123P.R.China State Key Laboratory State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing Ultra-high Voltage Bureau of Chongqing Electric Power Company Chongqing400050P.R.China

ISBN: (纸本)9781424438631;9781424438624

Dissolved gas analysis in transformer oil (DGA) is an important method for power transformer insulating diagnosis. Aiming at the problem that fuzzy c-means (FcM) clustering algorithm is likely to fall into local minimum point when being used for dissolved gas analysis,dynamic tunneling algorithm was introduced for its high global optimization performance. Then a FcM clustering algorithm was presented based on these two algorithms. On the Basis of local minimum obtained by optimization searching of FcM algorithm,using dynamic tunneling process to search a lower energy valley,then the value was submitted to FcM algorithm for iterative optimization until global minimum point was found by repeating the process. Through the application of this algorithm for DGA in transformer oil,transformer fault diagnosis can be achieved. These tests for fault diagnosis of chromatography transformer oil and noise samples show that,the algorithm can cluster samples quickly and effectively and with high accuracy for diagnosis.

关键词： Power transformer Dissolved gas analysis fuzzy c-means clustering algorithm dynamic tunneling system Fault diagnosis

来源：评论

学校读者我要写书评

暂无评论

A Study of New Forecast Model on Paroxysmal Accident

International Journal of Systems and Control

引用

International Journal of Systems and control 2007年第2期 164-168页

作者： Wu Shixiong Department of Automation,Information Science and Technic School of Xiamen University Xiamen, Fujian, 361005, china

Traditional forecast method has a disadvantage for the all-aroundconsideration,such as complication of algorithm, forecast precision,real-time application. There is a general consensus that the lack of a effective method to meet the *** in this paper there will be a discussion on the new model base on fuzzy-neural network. fuzzy-neural network is incorporated into a new paroxysmal accident forecast model as a fuzzy logic and neural *** selection of input parameters are analyzed with necessary considerations accorded history data and emergent events to reduce calculation time and improve the forecast precision

关键词： fuzzy neural network forecast model Kohonen algorithm Grossberg algorithm fuzzy c-means clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：