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Fractional gravitational search-radial basis neural network for bone marrow white blood cell classification

The Journal of Photographic Science 2018年第2期66卷 106-124页

ABSTRAcTABSTRAcTAutomotive image segmentation systems are becoming an important tool in the medical field for disease diagnosis. The white blood cell (WBc) segmentation is crucial, because it plays an important role in the determination of the diseases and helps experts to diagnose the blood disease disorders. The precise segmentation of the WBcs is quite challenging because of the complex contents in the bone marrow smears. In this paper, a novel neural network (NN) classifier is proposed for the classification of the bone marrow WBcs. The proposed NN classifier integrates the fractional gravitation search (FGS) algorithm for updating the weight in the radial basis function mapping for the classification of the WBc based on the cell nucleus feature. The experimentation of the proposed FGS-RBNN classifier is carried on the images collected from the publically available dataset. The performance of the proposed methodology is evaluated over the existing classifier approaches using the measures accuracy, sensitivity, and specificity. The results show that the classification using the nucleus features alone can be utilized to achieve the classification with the better accuracy. Moreover, the classification performance of the proposed FGS-RBNN is better than the existing classifiers, and it is proved to be the efficacious classifier with a classification accuracy of 95%.

关键词： Radial basis network gravitational search algorithm bone marrow white blood cells fuzzy c-means clustering algorithm

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An improved FcM algorithm with adaptive weights based on SA-PSO

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NEURAL cOMPUTING & APPLIcATIONS 2017年第10期28卷 3113-3118页

作者： Wu, Ziheng Wu, Zhongcheng Zhang, Jun Chinese Acad Sci High Magnet Field Lab Hefei Anhui Peoples R China Univ Sci & Technol China Hefei Anhui Peoples R China

fuzzy c-means clustering algorithm (FcM) often used in pattern recognition is an important method that has been successfully used in large amounts of practical applications. The FcM algorithm assumes that the significance of each data point is equal, which is obviously inappropriate from the viewpoint of adaptively adjusting the importance of each data point. In this paper, considering the different importance of each data point, a new clustering algorithm based on FcM is proposed, in which an adaptive weight vector W and an adaptive exponent p are introduced and the optimal values of the fuzziness parameter m and adaptive exponent p are determined by SA-PSO when the objective function reaches its minimum value. In this method, the particle swarm optimization (PSO) is integrated with simulated annealing (SA), which can improve the global search ability of PSO. Experimental results have demonstrated that the proposed algorithm can avoid local optima and significantly improve the clustering performance.

关键词： fuzzy c-means clustering algorithm Particle swarm optimization Simulated annealing Adaptive weight

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Identification of coherent Generators Using fuzzy c-means clustering algorithm and construction of Dynamic Equivalent of Power System

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JOURNAL OF ELEcTRIcAL SYSTEMS 2010年第2期6卷 1-18页

作者： El-arini, Mahdi M. M. Fathy, Ahmed Zagazig Univ Fac Engn Elect Power & MC Dept Zagazig Egypt

This paper presents a new simple method to identify the coherent groups of generators based on two different techniques;the first one is based on six proposed coherency criterions introduced by using time response of the linearized power system model;the second one is based on the application of fuzzy c-means clustering algorithm (FcM). A new technique of constructing the dynamic equivalent of power system is also presented in this work. The results obtained by the first technique are compared to those obtained by FcM technique. The proposed method is applied on 14-Bus IEEE power system. The obtained results proved that the proposed method is highly effective in determining the coherent groups of generators and in constructing the dynamic equivalent of power system with high accuracy.

关键词： coherent groups of generator fuzzy c-means clustering algorithm Dynamic equivalents Network reduction Dynamic aggregation

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Power transformer fault diagnosis using FcM and improved PcA

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JOURNAL OF ENGINEERING-JOE 2017年第14期2017卷 2605-2608页

作者： Kari, Tusongjiang Gao, Wensheng Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

In order to improve fault diagnosis accuracy of power transformer, a new fault diagnosis model based on fuzzy c-means (FcM) clustering algorithm and improved principal component analysis (IPcA) is presented. First, dissolve gas analysis samples are clustered with FcM and cluster centre for each fault type is regarded as reference sequence. Then, the IPcA approach is implemented to obtain main principal components containing 95% of original information. Finally, Euclidean distances between principal components of reference sequence and testing sample are calculated to identify final fault type. cases studies and test results show that the proposed approach achieves recognition of transformer fault effectively and has a higher diagnostic accuracy than the international electrotechnical commission (IEc) ratio method and the improved three ratio method.

关键词： fault diagnosis principal component analysis fuzzy set theory pattern clustering chemical analysis power transformer testing power transformer fault diagnosis model FcM clustering algorithm fuzzy c-means clustering algorithm improved principal component analysis dissolve gas analysis sample clustering IPcA approach Euclidean distance IEc ratio method

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A Novel Neural Network Approach to Transformer Fault Diagnosis Based on Momentum-Embedded BP Neural Network Optimized by Genetic algorithm and fuzzy c-means

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ARABIAN JOURNAL FOR ScIENcE AND ENGINEERING 2016年第9期41卷 3451-3461页

作者： Zhang, Kefei Yuan, Fang Guo, Jiang Wang, Guoping Wuhan Univ Coll Power & Mech Engn Wuhan 430072 Hubei Peoples R China

Transformer is one of the critical equipments for electric power transmission and distribution, and its safety situation plays an important role on stability and security level of power systems. Therefore, the diagnosis of its abnormal situation has always drawn enormous attention from both domestic and international scholars. Dissolved gas analysis (DGA) is a widely used method in transformer fault diagnosis field. However, the conventional DGA is not well suitable for transformer fault diagnosis because transformer's structure is complex and operating environment is changeable. On the other hand, the back propagation (BP) neural network, frequently employed in related field, also has some inherent disadvantages, such as local optimization, over-fitting and difficulties in convergence. So simply integrating conventional DGA to BP is not a good approach for fault diagnosis. Moreover, disturbance or noises within the training data, which is unavoidable due to systematic errors, may greatly influence the accuracy of diagnosis model with the growing size of the data. Thus, in this study, we integrate a combination ratio of taking advantages of IEc and Doernenburg, instead of usual DGA, into genetic algorithm (GA) and fuzzy c-means clustering algorithm (FcM) optimized BP, successfully building a novel model which has not been reported previously. Our results show this model has a better diagnosis accuracy rate and generalization ability than other models, and FcM and GA can significantly overcome the disadvantages of training data and BP, offering the potential of implementation for real-time diagnosis systems.

关键词： Transformers Fault diagnosis Neural network Genetic algorithm Dissolved gas analysis fuzzy c-means clustering algorithm combination ratio method

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Optimal cluster Power for Joint Spectrum Sensing and Secondary Data Transmission in cognitive Radio Networks 11

Optimal Cluster Power for Joint Spectrum Sensing and Seconda...

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IEEE International conference on Advanced Networks and Telecommunications Systems (ANTS)

作者： Paul, Anal Maity, Santi P. Indian Inst Engn Sci & Technol Dept Informat Technol Howrah 711103 WB India

ISBN: (纸本)9781538623480;9781538623473

This paper considers the problem of joint spectrum sensing and secondary data transmission in the relay-assisted cognitive radio networks. An optimization problem is formulated that searches for the cluster-wise relay selection and consequent power allocation with an objective to maximize the sum throughput of the secondary network under the constraints of the interference power limit and the probability of primary user's (PU) signal detection. fuzzy c-means clustering algorithm is applied to associate a set of secondary users (SUs) to form a cluster at the source as well as in destination end followed by their link establishment via a particular relay. closed form expression for the optimal cluster power allocation is derived and the performance of the proposed system is investigated in terms of secondary network throughput and sum cluster power for SU data transmission. A large set of simulation results show that a gain similar to 24.37% and similar to 36.03% in SU throughput are achieved for the proposed scheme when compared to the existing works.

关键词： cognitive radio networks spectrum sensing Relay-assisted network fuzzy c-means clustering algorithm

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An Improved-WM Method Based on Optimization of centers of Output fuzzy Subsets for fuzzy Rules

An Improved-WM Method Based on Optimization of Centers of Ou...

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20th World congress of the International-Federation-of-Automatic-control (IFAc)

作者： Jin, Yali cao, Weihua Wu, Min She, Jinhua Yuan, Yan China Univ Geosci Sch Automat Wuhan 430074 Hubei Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Hubei Peoples R China Tokyo Univ Technol Sch Engn Hachioji Tokyo 1920982 Japan

The generation of fuzzy rules from samples is significant for fuzzy modelling. To improve the robustness of Wang-Mendel (WM) method, an improved WM method to extract fuzzy rules from all the regularized sample data was proposed. However, the accuracy of the model with this method is degraded for the conflicting rules with small difference between support degrees. And the output subsets can only be chosen from the pre-defined ones. To solve these problems, we develop an improved-WM method based on optimization of centers of output fuzzy subsets for fuzzy rules (cOiWM). This method adopts the fuzzy c-means (FcM) clustering algorithm to divide the input and output spaces, and the improved WM method which replaces the original data by regularized data is used to calculate the support degrees. Then the support degrees are used as weights to optimize the centers of output fuzzy subsets with a method of weighted averages, so as to enhance the accuracy of a fuzzy model. Experimental results of a case study on short term daily maximum electric load forecasting prove that our proposed method enhances the accuracy of a fuzzy model. (c) 2017, IFAc (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： fuzzy rules Wang-Mendel method fuzzy c-means clustering algorithm conflicting rules Accuracy

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Modeling of Penicillin Fermentation Process Based on FcM and Improved Just-In-Time Learning algorithm 36

Modeling of Penicillin Fermentation Process Based on FCM and...

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第36届中国控制会议

作者： Dapeng Niu Huiyuan Gao Yuanqing Liu School of Information Science ＆ Engineering Northeastern University

ISBN: (纸本)9781538629185

Penicillin fermentation process has the characteristics of time variation,non-linearity and *** accurate mechanism model is quite difficult to *** order to establish a rapid and accurate model to describe the characteristics of penicillin fermentation process,a local modeling method based on Just-In-Time algorithm is proposed for product concentration prediction during penicillin fermentation *** the segmentation characteristics penicillin fermentation process,fuzzy c-means clustering algorithm is used to classify the historical *** the process of on-line modeling,the historical database is selected from the corresponding fermentation period and the adjacent time *** least squares support vector machines method is used for local modeling to improve the real-time performance of the *** simulation results show that the prediction model for penicillin product concentration based on FcM and improved Just-In-Time algorithm is more effective in accuracy and real-time performances.

关键词： Penicillin fermentation fuzzy c-means clustering algorithm Just-In-Time learning Least Squares Support Vector Machines

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An Improved-WM Method Based on Optimization of centers of Output fuzzy Subsets for fuzzy Rules

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IFAc-PapersOnLine 2017年第1期50卷 7369-7374页

作者： Jin Y. cao W. Wu M. She J. Yuan Y. School of Automation China University of Geosciences Wuhan 430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China School of Engineering Tokyo University of Technology Hachioji Tokyo 192-0982 Japan

关键词： Accuracy conflicting rules fuzzy c-means clustering algorithm fuzzy rules Wang-Mendel method

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Brain MRI image segmentation based on improved fuzzy c-means algorithm

Brain MRI image segmentation based on improved Fuzzy C-means...

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International conference on Smart city and Systems Engineering (IcScSE)

作者： Sun, Shiling Yan, Shuxun Wang, Ying Li, Yun Henan Coll Tradit Chinese Med Zhengzhou 450008 Henan Peoples R China Zhengzhou Univ Affiliated Hosp 1 Zhengzhou 450000 Henan Peoples R China

ISBN: (纸本)9781509055302

Image segmentation is a challenge and difficult task in image processing, and the foundation of image analysis and identifying. This paper mainly studies the means clustering image segmentation. In view of the traditional clustering image segmentation algorithm for image segmentation accuracy is low problem, put forward a kind of fuzzy control based on c-means clustering image segmentation method. Methods firstly in clustering image segmentation algorithm based on fast, using fuzzy c-means clustering algorithm for image segmentation. The experimental results show that the algorithm in clustering, to optimize the performance of the same premise, image segmentation edge clear, segmentation better than traditional clustering algorithm for image segmentation.

关键词： image segmentation fuzzy c-means clustering algorithm optimization

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