Extended Fuzzy Clustering Algorithms
U. Kaymak (Uzay); M. Setnes
2000-01-01
textabstractFuzzy clustering is a widely applied method for obtaining fuzzy models from data. It has been applied successfully in various fields including finance and marketing. Despite the successful applications, there are a number of issues that must be dealt with in practical applications of fuz
Intuitionistic fuzzy hierarchical clustering algorithms
Institute of Scientific and Technical Information of China (English)
Xu Zeshui
2009-01-01
Intuitionistic fuzzy set (IFS) is a set of 2-tuple arguments, each of which is characterized by a mem-bership degree and a nonmembership degree. The generalized form of IFS is interval-valued intuitionistic fuzzy set (IVIFS), whose components are intervals rather than exact numbers. IFSs and IVIFSs have been found to be very useful to describe vagueness and uncertainty. However, it seems that little attention has been focused on the clus-tering analysis of IFSs and IVIFSs. An intuitionistic fuzzy hierarchical algorithm is introduced for clustering IFSs, which is based on the traditional hierarchical clustering procedure, the intuitionistic fuzzy aggregation operator, and the basic distance measures between IFSs: the Hamming distance, normalized Hamming, weighted Hamming, the Euclidean distance, the normalized Euclidean distance, and the weighted Euclidean distance. Subsequently, the algorithm is extended for clustering IVIFSs. Finally the algorithm and its extended form are applied to the classifications of building materials and enterprises respectively.
Kernel method-based fuzzy clustering algorithm
Institute of Scientific and Technical Information of China (English)
Wu Zhongdong; Gao Xinbo; Xie Weixin; Yu Jianping
2005-01-01
The fuzzy C-means clustering algorithm(FCM) to the fuzzy kernel C-means clustering algorithm(FKCM) to effectively perform cluster analysis on the diversiform structures are extended, such as non-hyperspherical data, data with noise, data with mixture of heterogeneous cluster prototypes, asymmetric data, etc. Based on the Mercer kernel, FKCM clustering algorithm is derived from FCM algorithm united with kernel method. The results of experiments with the synthetic and real data show that the FKCM clustering algorithm is universality and can effectively unsupervised analyze datasets with variform structures in contrast to FCM algorithm. It is can be imagined that kernel-based clustering algorithm is one of important research direction of fuzzy clustering analysis.
Hesitant fuzzy agglomerative hierarchical clustering algorithms
Zhang, Xiaolu; Xu, Zeshui
2015-02-01
Recently, hesitant fuzzy sets (HFSs) have been studied by many researchers as a powerful tool to describe and deal with uncertain data, but relatively, very few studies focus on the clustering analysis of HFSs. In this paper, we propose a novel hesitant fuzzy agglomerative hierarchical clustering algorithm for HFSs. The algorithm considers each of the given HFSs as a unique cluster in the first stage, and then compares each pair of the HFSs by utilising the weighted Hamming distance or the weighted Euclidean distance. The two clusters with smaller distance are jointed. The procedure is then repeated time and again until the desirable number of clusters is achieved. Moreover, we extend the algorithm to cluster the interval-valued hesitant fuzzy sets, and finally illustrate the effectiveness of our clustering algorithms by experimental results.
Intuitionistic Fuzzy Possibilistic C Means Clustering Algorithms
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Arindam Chaudhuri
2015-01-01
Full Text Available Intuitionistic fuzzy sets (IFSs provide mathematical framework based on fuzzy sets to describe vagueness in data. It finds interesting and promising applications in different domains. Here, we develop an intuitionistic fuzzy possibilistic C means (IFPCM algorithm to cluster IFSs by hybridizing concepts of FPCM, IFSs, and distance measures. IFPCM resolves inherent problems encountered with information regarding membership values of objects to each cluster by generalizing membership and nonmembership with hesitancy degree. The algorithm is extended for clustering interval valued intuitionistic fuzzy sets (IVIFSs leading to interval valued intuitionistic fuzzy possibilistic C means (IVIFPCM. The clustering algorithm has membership and nonmembership degrees as intervals. Information regarding membership and typicality degrees of samples to all clusters is given by algorithm. The experiments are performed on both real and simulated datasets. It generates valuable information and produces overlapped clusters with different membership degrees. It takes into account inherent uncertainty in information captured by IFSs. Some advantages of algorithms are simplicity, flexibility, and low computational complexity. The algorithm is evaluated through cluster validity measures. The clustering accuracy of algorithm is investigated by classification datasets with labeled patterns. The algorithm maintains appreciable performance compared to other methods in terms of pureness ratio.
A new fusion algorithm for fuzzy clustering
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Ivan Vidović
2014-12-01
Full Text Available In this paper, we have considered the merging problem of two ellipsoidal clusters in order to construct a new fusion algorithm for fuzzy clustering. We have proposed a criterion for merging two ellipsoidal clusters ∏1, ∏2 with associated main Mahalanobis circles Ej(cj,σj, where cj is the centroid and σ^2j is the Mahalanobis variance of cluster ∏j . Based on the well-known Davies-Bouldin index, we have constructed a new fusion algorithm. The criterion has been tested on several data sets, and the performance of the fusion algorithm has been demonstrated on an illustrative example.
Fuzzy Rules for Ant Based Clustering Algorithm
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Amira Hamdi
2016-01-01
Full Text Available This paper provides a new intelligent technique for semisupervised data clustering problem that combines the Ant System (AS algorithm with the fuzzy c-means (FCM clustering algorithm. Our proposed approach, called F-ASClass algorithm, is a distributed algorithm inspired by foraging behavior observed in ant colonyT. The ability of ants to find the shortest path forms the basis of our proposed approach. In the first step, several colonies of cooperating entities, called artificial ants, are used to find shortest paths in a complete graph that we called graph-data. The number of colonies used in F-ASClass is equal to the number of clusters in dataset. Hence, the partition matrix of dataset founded by artificial ants is given in the second step, to the fuzzy c-means technique in order to assign unclassified objects generated in the first step. The proposed approach is tested on artificial and real datasets, and its performance is compared with those of K-means, K-medoid, and FCM algorithms. Experimental section shows that F-ASClass performs better according to the error rate classification, accuracy, and separation index.
Mercer Kernel Based Fuzzy Clustering Self-Adaptive Algorithm
Institute of Scientific and Technical Information of China (English)
李侃; 刘玉树
2004-01-01
A novel mercer kernel based fuzzy clustering self-adaptive algorithm is presented. The mercer kernel method is introduced to the fuzzy c-means clustering. It may map implicitly the input data into the high-dimensional feature space through the nonlinear transformation. Among other fuzzy c-means and its variants, the number of clusters is first determined. A self-adaptive algorithm is proposed. The number of clusters, which is not given in advance, can be gotten automatically by a validity measure function. Finally, experiments are given to show better performance with the method of kernel based fuzzy c-means self-adaptive algorithm.
A dynamic fuzzy clustering method based on genetic algorithm
Institute of Scientific and Technical Information of China (English)
ZHENG Yan; ZHOU Chunguang; LIANG Yanchun; GUO Dongwei
2003-01-01
A dynamic fuzzy clustering method is presented based on the genetic algorithm. By calculating the fuzzy dissimilarity between samples the essential associations among samples are modeled factually. The fuzzy dissimilarity between two samples is mapped into their Euclidean distance, that is, the high dimensional samples are mapped into the two-dimensional plane. The mapping is optimized globally by the genetic algorithm, which adjusts the coordinates of each sample, and thus the Euclidean distance, to approximate to the fuzzy dissimilarity between samples gradually. A key advantage of the proposed method is that the clustering is independent of the space distribution of input samples, which improves the flexibility and visualization. This method possesses characteristics of a faster convergence rate and more exact clustering than some typical clustering algorithms. Simulated experiments show the feasibility and availability of the proposed method.
AN IMPROVED FUZZY CLUSTERING ALGORITHM FOR MICROARRAY IMAGE SPOTS SEGMENTATION
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V.G. Biju
2015-11-01
Full Text Available An automatic cDNA microarray image processing using an improved fuzzy clustering algorithm is presented in this paper. The spot segmentation algorithm proposed uses the gridding technique developed by the authors earlier, for finding the co-ordinates of each spot in an image. Automatic cropping of spots from microarray image is done using these co-ordinates. The present paper proposes an improved fuzzy clustering algorithm Possibility fuzzy local information c means (PFLICM to segment the spot foreground (FG from background (BG. The PFLICM improves fuzzy local information c means (FLICM algorithm by incorporating typicality of a pixel along with gray level information and local spatial information. The performance of the algorithm is validated using a set of simulated cDNA microarray images added with different levels of AWGN noise. The strength of the algorithm is tested by computing the parameters such as the Segmentation matching factor (SMF, Probability of error (pe, Discrepancy distance (D and Normal mean square error (NMSE. SMF value obtained for PFLICM algorithm shows an improvement of 0.9 % and 0.7 % for high noise and low noise microarray images respectively compared to FLICM algorithm. The PFLICM algorithm is also applied on real microarray images and gene expression values are computed.
AN IMPROVED ALGORITHM FOR SUPERVISED FUZZY C-MEANS CLUSTERING OF REMOTELY SENSED DATA
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
This paper describes an improved algorithm for fuzzy c-means clustering of remotely sensed data, by which the degree of fuzziness of the resultant classification is de creased as comparing with that by a conventional algorithm: that is , the classification accura cy is increased. This is achieved by incorporating covariance matrices at the level of individual classes rather than assuming a global one. Empirical results from a fuzzy classification of an Edinburgh suburban land cover confirmed the improved performance of the new algorithm for fuzzy c-means clustering, in particular when fuzziness is also accommodated in the assumed reference data.
Classification of posture maintenance data with fuzzy clustering algorithms
Bezdek, James C.
1992-01-01
Sensory inputs from the visual, vestibular, and proprioreceptive systems are integrated by the central nervous system to maintain postural equilibrium. Sustained exposure to microgravity causes neurosensory adaptation during spaceflight, which results in decreased postural stability until readaptation occurs upon return to the terrestrial environment. Data which simulate sensory inputs under various sensory organization test (SOT) conditions were collected in conjunction with Johnson Space Center postural control studies using a tilt-translation device (TTD). The University of West Florida applied the fuzzy c-meams (FCM) clustering algorithms to this data with a view towards identifying various states and stages of subjects experiencing such changes. Feature analysis, time step analysis, pooling data, response of the subjects, and the algorithms used are discussed.
A Novel Cluster Head Selection Algorithm Based on Fuzzy Clustering and Particle Swarm Optimization.
Ni, Qingjian; Pan, Qianqian; Du, Huimin; Cao, Cen; Zhai, Yuqing
2017-01-01
An important objective of wireless sensor network is to prolong the network life cycle, and topology control is of great significance for extending the network life cycle. Based on previous work, for cluster head selection in hierarchical topology control, we propose a solution based on fuzzy clustering preprocessing and particle swarm optimization. More specifically, first, fuzzy clustering algorithm is used to initial clustering for sensor nodes according to geographical locations, where a sensor node belongs to a cluster with a determined probability, and the number of initial clusters is analyzed and discussed. Furthermore, the fitness function is designed considering both the energy consumption and distance factors of wireless sensor network. Finally, the cluster head nodes in hierarchical topology are determined based on the improved particle swarm optimization. Experimental results show that, compared with traditional methods, the proposed method achieved the purpose of reducing the mortality rate of nodes and extending the network life cycle.
Application of a New Fuzzy Clustering Algorithm in Intrusion Detection
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
This paper presents a new Section Set Adaptive FCM algorithm. The algorithm solved the shortcomings of localoptimality, unsure classification and clustering numbers ascertained previously. And it improved on the architecture of FCM al-gorithm, enhanced the analysis for effective clustering. During the clustering processing, it may adjust clustering numbers dy-namically. Finally, it used the method of section set decreasing the time of classification. By experiments, the algorithm can im-prove dependability of clustering and correctness of classification.
An Extension of the Fuzzy Possibilistic Clustering Algorithm Using Type-2 Fuzzy Logic Techniques
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Elid Rubio
2017-01-01
Full Text Available In this work an extension of the Fuzzy Possibilistic C-Means (FPCM algorithm using Type-2 Fuzzy Logic Techniques is presented, and this is done in order to improve the efficiency of FPCM algorithm. With the purpose of observing the performance of the proposal against the Interval Type-2 Fuzzy C-Means algorithm, several experiments were made using both algorithms with well-known datasets, such as Wine, WDBC, Iris Flower, Ionosphere, Abalone, and Cover type. In addition some experiments were performed using another set of test images to observe the behavior of both of the above-mentioned algorithms in image preprocessing. Some comparisons are performed between the proposed algorithm and the Interval Type-2 Fuzzy C-Means (IT2FCM algorithm to observe if the proposed approach has better performance than this algorithm.
Using genetic algorithm based fuzzy adaptive resonance theory for clustering analysis
Institute of Scientific and Technical Information of China (English)
LIU Bo; WANG Yong; WANG Hong-jian
2006-01-01
In the clustering applications field, fuzzy adaptive resonance theory system has been widely applied. But, three parameters of fuzzy adaptive resonance theory need to be adjusted manually for obtaining better clustering. It needs much time to test and does not assure a best result. Genetic algorithm is an optimal mathematical search technique based on the principles of natural selection and genetic recombination. So, to make the fuzzy adaptive resonance theory parameters choosing process automation, an approach incorporating genetic algorithm and fuzzy adaptive resonance theory neural network has been applied. Then, the best clustering result can be obtained.Through experiment, it can be proved that the most appropriate parameters of fuzzy adaptive resonance theory can be gained effectively by this approach.
Possibilistic Exponential Fuzzy Clustering
Institute of Scientific and Technical Information of China (English)
Kiatichai Treerattanapitak; Chuleerat Jaruskulchai
2013-01-01
Generally,abnormal points (noise and outliers) cause cluster analysis to produce low accuracy especially in fuzzy clustering.These data not only stay in clusters but also deviate the centroids from their true positions.Traditional fuzzy clustering like Fuzzy C-Means (FCM) always assigns data to all clusters which is not reasonable in some circumstances.By reformulating objective function in exponential equation,the algorithm aggressively selects data into the clusters.However noisy data and outliers cannot be properly handled by clustering process therefore they are forced to be included in a cluster because of a general probabilistic constraint that the sum of the membership degrees across all clusters is one.In order to improve this weakness,possibilistic approach relaxes this condition to improve membership assignment.Nevertheless,possibilistic clustering algorithms generally suffer from coincident clusters because their membership equations ignore the distance to other clusters.Although there are some possibilistic clustering approaches that do not generate coincident clusters,most of them require the right combination of multiple parameters for the algorithms to work.In this paper,we theoretically study Possibilistic Exponential Fuzzy Clustering (PXFCM) that integrates possibilistic approach with exponential fuzzy clustering.PXFCM has only one parameter and not only partitions the data but also filters noisy data or detects them as outliers.The comprehensive experiments show that PXFCM produces high accuracy in both clustering results and outlier detection without generating coincident problems.
DEFF Research Database (Denmark)
Berks, G.; Keyserlingk, Diedrich Graf von; Jantzen, Jan
2000-01-01
A symptom is a condition indicating the presence of a disease, especially, when regarded as an aid in diagnosis.Symptoms are the smallest units indicating the existence of a disease. A syndrome on the other hand is an aggregate, set or cluster of concurrent symptoms which together indicate...... and clustering are the basic concerns in medicine. Classification depends on definitions of the classes and their required degree of participant of the elements in the cases' symptoms. In medicine imprecise conditions are the rule and therefore fuzzy methods are much more suitable than crisp ones. Fuzzy c......-mean clustering is an easy and well improved tool, which has been applied in many medical fields. We used c-mean fuzzy clustering after feature extraction from an aphasia database. Factor analysis was applied on a correlation matrix of 26 symptoms of language disorders and led to five factors. The factors...
A Self-Adaptive Fuzzy c-Means Algorithm for Determining the Optimal Number of Clusters
Wang, Zhihao; Yi, Jing
2016-01-01
For the shortcoming of fuzzy c-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rule n and obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function. Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process. At last, experiments were done on the UCI, KDD Cup 1999, and synthetic datasets, which showed that the method not only effectively determined the optimal number of clusters, but also reduced the iteration of FCM with the stable clustering result. PMID:28042291
Improved FIFO Scheduling Algorithm Based on Fuzzy Clustering in Cloud Computing
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Jian Li
2017-02-01
Full Text Available In cloud computing, some large tasks may occupy too many resources and some small tasks may wait for a long time based on First-In-First-Out (FIFO scheduling algorithm. To reduce tasks’ waiting time, we propose a task scheduling algorithm based on fuzzy clustering algorithms. We construct a task model, resource model, and analyze tasks’ preference, then classify resources with fuzzy clustering algorithms. Based on the parameters of cloud tasks, the algorithm will calculate resource expectation and assign tasks to different resource clusters, so the complexity of resource selection will be decreased. As a result, the algorithm will reduce tasks’ waiting time and improve the resource utilization. The experiment results show that the proposed algorithm shortens the execution time of tasks and increases the resource utilization.
Risk Assessment for Bridges Safety Management during Operation Based on Fuzzy Clustering Algorithm
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Xia Hanyu
2016-01-01
Full Text Available In recent years, large span and large sea-crossing bridges are built, bridges accidents caused by improper operational management occur frequently. In order to explore the better methods for risk assessment of the bridges operation departments, the method based on fuzzy clustering algorithm is selected. Then, the implementation steps of fuzzy clustering algorithm are described, the risk evaluation system is built, and Taizhou Bridge is selected as an example, the quantitation of risk factors is described. After that, the clustering algorithm based on fuzzy equivalence is calculated on MATLAB 2010a. In the last, Taizhou Bridge operation management departments are classified and sorted according to the degree of risk, and the safety situation of operation departments is analyzed.
An Improved Fuzzy c-Means Clustering Algorithm Based on Shadowed Sets and PSO
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Jian Zhang
2014-01-01
Full Text Available To organize the wide variety of data sets automatically and acquire accurate classification, this paper presents a modified fuzzy c-means algorithm (SP-FCM based on particle swarm optimization (PSO and shadowed sets to perform feature clustering. SP-FCM introduces the global search property of PSO to deal with the problem of premature convergence of conventional fuzzy clustering, utilizes vagueness balance property of shadowed sets to handle overlapping among clusters, and models uncertainty in class boundaries. This new method uses Xie-Beni index as cluster validity and automatically finds the optimal cluster number within a specific range with cluster partitions that provide compact and well-separated clusters. Experiments show that the proposed approach significantly improves the clustering effect.
Risk analysis of dam based on artificial bee colony algorithm with fuzzy c-means clustering
Energy Technology Data Exchange (ETDEWEB)
Li, Haojin; Li, Junjie; Kang, Fei
2011-05-15
Risk analysis is a method which has been incorporated into infrastructure engineering. Fuzzy c-means clustering (FCM) is a simple and fast method utilized most of the time, but it can induce errors as it is sensitive to initialization. The aim of this paper was to propose a new method for risk analysis using an artificial bee colony algorithm (ABC) with FCM. This new technique is first explained and then applied on three experiments. Results demonstrated that the combination of artificial bee colony algorithm fuzzy c-means clustering (ABCFCM) is overcoming the FCM issue since it is not initialization sensitive and experiments showed that this algorithm is more accurate and than FCM. This paper provides a new tool for risk analysis which can be used for risk prioritizing and reinforcing dangerous dams in a more scientific way.
Wang, Deguang; Han, Baochang; Huang, Ming
Computer forensics is the technology of applying computer technology to access, investigate and analysis the evidence of computer crime. It mainly include the process of determine and obtain digital evidence, analyze and take data, file and submit result. And the data analysis is the key link of computer forensics. As the complexity of real data and the characteristics of fuzzy, evidence analysis has been difficult to obtain the desired results. This paper applies fuzzy c-means clustering algorithm based on particle swarm optimization (FCMP) in computer forensics, and it can be more satisfactory results.
Fuzzy-Logic Based Distributed Energy-Efficient Clustering Algorithm for Wireless Sensor Networks
Zhang, Ying; Wang, Jun; Han, Dezhi; Wu, Huafeng; Zhou, Rundong
2017-01-01
Due to the high-energy efficiency and scalability, the clustering routing algorithm has been widely used in wireless sensor networks (WSNs). In order to gather information more efficiently, each sensor node transmits data to its Cluster Head (CH) to which it belongs, by multi-hop communication. However, the multi-hop communication in the cluster brings the problem of excessive energy consumption of the relay nodes which are closer to the CH. These nodes’ energy will be consumed more quickly than the farther nodes, which brings the negative influence on load balance for the whole networks. Therefore, we propose an energy-efficient distributed clustering algorithm based on fuzzy approach with non-uniform distribution (EEDCF). During CHs’ election, we take nodes’ energies, nodes’ degree and neighbor nodes’ residual energies into consideration as the input parameters. In addition, we take advantage of Takagi, Sugeno and Kang (TSK) fuzzy model instead of traditional method as our inference system to guarantee the quantitative analysis more reasonable. In our scheme, each sensor node calculates the probability of being as CH with the help of fuzzy inference system in a distributed way. The experimental results indicate EEDCF algorithm is better than some current representative methods in aspects of data transmission, energy consumption and lifetime of networks. PMID:28671641
Fuzzy-Logic Based Distributed Energy-Efficient Clustering Algorithm for Wireless Sensor Networks.
Zhang, Ying; Wang, Jun; Han, Dezhi; Wu, Huafeng; Zhou, Rundong
2017-07-03
Due to the high-energy efficiency and scalability, the clustering routing algorithm has been widely used in wireless sensor networks (WSNs). In order to gather information more efficiently, each sensor node transmits data to its Cluster Head (CH) to which it belongs, by multi-hop communication. However, the multi-hop communication in the cluster brings the problem of excessive energy consumption of the relay nodes which are closer to the CH. These nodes' energy will be consumed more quickly than the farther nodes, which brings the negative influence on load balance for the whole networks. Therefore, we propose an energy-efficient distributed clustering algorithm based on fuzzy approach with non-uniform distribution (EEDCF). During CHs' election, we take nodes' energies, nodes' degree and neighbor nodes' residual energies into consideration as the input parameters. In addition, we take advantage of Takagi, Sugeno and Kang (TSK) fuzzy model instead of traditional method as our inference system to guarantee the quantitative analysis more reasonable. In our scheme, each sensor node calculates the probability of being as CH with the help of fuzzy inference system in a distributed way. The experimental results indicate EEDCF algorithm is better than some current representative methods in aspects of data transmission, energy consumption and lifetime of networks.
New two-dimensional fuzzy C-means clustering algorithm for image segmentation
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
To solve the problem of poor anti-noise performance of the traditional fuzzy C-means (FCM) algorithm in image segmentation,a novel two-dimensional FCM clustering algorithm for image segmentation was proposed.In this method,the image segmentation was converted into an optimization problem.The fitness function containing neighbor information was set up based on the gray information and the neighbor relations between the pixcls described by the improved two-dimensional histogram.By making use of the global searching ability of the predator-prey particle swarm optimization,the optimal cluster center could be obtained by iterative optimization,and the image segmentation could be accomplished.The simulation results show that the segmentation accuracy ratio of the proposed method is above 99%.The proposed algorithm has strong anti-noise capability,high clustering accuracy and good segment effect,indicating that it is an effective algorithm for image segmentation.
Clustering of tethered satellite system simulation data by an adaptive neuro-fuzzy algorithm
Mitra, Sunanda; Pemmaraju, Surya
1992-01-01
Recent developments in neuro-fuzzy systems indicate that the concepts of adaptive pattern recognition, when used to identify appropriate control actions corresponding to clusters of patterns representing system states in dynamic nonlinear control systems, may result in innovative designs. A modular, unsupervised neural network architecture, in which fuzzy learning rules have been embedded is used for on-line identification of similar states. The architecture and control rules involved in Adaptive Fuzzy Leader Clustering (AFLC) allow this system to be incorporated in control systems for identification of system states corresponding to specific control actions. We have used this algorithm to cluster the simulation data of Tethered Satellite System (TSS) to estimate the range of delta voltages necessary to maintain the desired length rate of the tether. The AFLC algorithm is capable of on-line estimation of the appropriate control voltages from the corresponding length error and length rate error without a priori knowledge of their membership functions and familarity with the behavior of the Tethered Satellite System.
An Airborne Radar Clutter Tracking Algorithm Based on Multifractal and Fuzzy C-Mean Cluster
Institute of Scientific and Technical Information of China (English)
Wei Zhang; Sheng-Lin Yu; Gong Zhang
2007-01-01
For an airborne lookdown radar, clutter power often changes dynamically about 80 dB with wide distributions as the platform moves. Therefore, clutter tracking techniques are required to guide the selection of const false alarm rate (CFAR) schemes. In this work, clutter tracking is done in image domain and an algorithm combining multifractal and fuzzy C-mean (FCM) cluster is proposed. The clutter with large dynamic distributions in power density is converted to steady distributions of multifractal exponents by the multifractal transformation with the optimum moment. Then, later, the main lobe and side lobe are tracked from the multifractal exponents by FCM clustering method.
Fuzzy Clustering Using C-Means Method
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Georgi Krastev
2015-05-01
Full Text Available The cluster analysis of fuzzy clustering according to the fuzzy c-means algorithm has been described in this paper: the problem about the fuzzy clustering has been discussed and the general formal concept of the problem of the fuzzy clustering analysis has been presented. The formulation of the problem has been specified and the algorithm for solving it has been described.
CONSIDERING NEIGHBORHOOD INFORMATION IN IMAGE FUZZY CLUSTERING
Institute of Scientific and Technical Information of China (English)
Huang Ning; Zhu Minhui; Zhang Shourong
2002-01-01
Fuzzy C-means clustering algorithm is a classical non-supervised classification method.For image classification, fuzzy C-means clustering algorithm makes decisions on a pixel-by-pixel basis and does not take advantage of spatial information, regardless of the pixels' correlation. In this letter, a novel fuzzy C-means clustering algorithm is introduced, which is based on image's neighborhood system. During classification procedure, the novel algorithm regards all pixels'fuzzy membership as a random field. The neighboring pixels' fuzzy membership information is used for the algorithm's iteration procedure. As a result, the algorithm gives a more smooth classification result and cuts down the computation time.
Sun, Jiajia; Li, Yaoguo
2017-02-01
Joint inversion that simultaneously inverts multiple geophysical data sets to recover a common Earth model is increasingly being applied to exploration problems. Petrophysical data can serve as an effective constraint to link different physical property models in such inversions. There are two challenges, among others, associated with the petrophysical approach to joint inversion. One is related to the multimodality of petrophysical data because there often exist more than one relationship between different physical properties in a region of study. The other challenge arises from the fact that petrophysical relationships have different characteristics and can exhibit point, linear, quadratic, or exponential forms in a crossplot. The fuzzy c-means (FCM) clustering technique is effective in tackling the first challenge and has been applied successfully. We focus on the second challenge in this paper and develop a joint inversion method based on variations of the FCM clustering technique. To account for the specific shapes of petrophysical relationships, we introduce several different fuzzy clustering algorithms that are capable of handling different shapes of petrophysical relationships. We present two synthetic and one field data examples and demonstrate that, by choosing appropriate distance measures for the clustering component in the joint inversion algorithm, the proposed joint inversion method provides an effective means of handling common petrophysical situations we encounter in practice. The jointly inverted models have both enhanced structural similarity and increased petrophysical correlation, and better represent the subsurface in the spatial domain and the parameter domain of physical properties.
Fuzzy clustering with Minkowski distance
P.J.F. Groenen (Patrick); U. Kaymak (Uzay); J.M. van Rosmalen (Joost)
2006-01-01
textabstractDistances in the well known fuzzy c-means algorithm of Bezdek (1973) are measured by the squared Euclidean distance. Other distances have been used as well in fuzzy clustering. For example, Jajuga (1991) proposed to use the L_1-distance and Bobrowski and Bezdek (1991) also used the L_inf
Intuitionistic fuzzy aggregation and clustering
Xu, Zeshui
2012-01-01
This book offers a systematic introduction to the clustering algorithms for intuitionistic fuzzy values, the latest research results in intuitionistic fuzzy aggregation techniques, the extended results in interval-valued intuitionistic fuzzy environments, and their applications in multi-attribute decision making, such as supply chain management, military system performance evaluation, project management, venture capital, information system selection, building materials classification, and operational plan assessment, etc.
Directory of Open Access Journals (Sweden)
Iman Aghayan
2012-11-01
Full Text Available This paper compares two fuzzy clustering algorithms – fuzzy subtractive clustering and fuzzy C-means clustering – to a multi-layer perceptron neural network for their ability to predict the severity of crash injuries and to estimate the response time on the traffic crash data. Four clustering algorithms – hierarchical, K-means, subtractive clustering, and fuzzy C-means clustering – were used to obtain the optimum number of clusters based on the mean silhouette coefficient and R-value before applying the fuzzy clustering algorithms. The best-fit algorithms were selected according to two criteria: precision (root mean square, R-value, mean absolute errors, and sum of square error and response time (t. The highest R-value was obtained for the multi-layer perceptron (0.89, demonstrating that the multi-layer perceptron had a high precision in traffic crash prediction among the prediction models, and that it was stable even in the presence of outliers and overlapping data. Meanwhile, in comparison with other prediction models, fuzzy subtractive clustering provided the lowest value for response time (0.284 second, 9.28 times faster than the time of multi-layer perceptron, meaning that it could lead to developing an on-line system for processing data from detectors and/or a real-time traffic database. The model can be extended through improvements based on additional data through induction procedure.
Lee, Chongdeuk; Jeong, Taegwon
2011-01-01
Clustering is an important mechanism that efficiently provides information for mobile nodes and improves the processing capacity of routing, bandwidth allocation, and resource management and sharing. Clustering algorithms can be based on such criteria as the battery power of nodes, mobility, network size, distance, speed and direction. Above all, in order to achieve good clustering performance, overhead should be minimized, allowing mobile nodes to join and leave without perturbing the membership of the cluster while preserving current cluster structure as much as possible. This paper proposes a Fuzzy Relevance-based Cluster head selection Algorithm (FRCA) to solve problems found in existing wireless mobile ad hoc sensor networks, such as the node distribution found in dynamic properties due to mobility and flat structures and disturbance of the cluster formation. The proposed mechanism uses fuzzy relevance to select the cluster head for clustering in wireless mobile ad hoc sensor networks. In the simulation implemented on the NS-2 simulator, the proposed FRCA is compared with algorithms such as the Cluster-based Routing Protocol (CBRP), the Weighted-based Adaptive Clustering Algorithm (WACA), and the Scenario-based Clustering Algorithm for Mobile ad hoc networks (SCAM). The simulation results showed that the proposed FRCA achieves better performance than that of the other existing mechanisms.
van Lith, Pascal; van Lith, P.F.; Betlem, Bernardus H.L.; Roffel, B.
2002-01-01
Hybrid fuzzy-first principles models can be a good alternative if a complete physical model is difficult to derive. These hybrid models consist of a framework of dynamic mass and energy balances, supplemented by fuzzy submodels describing additional equations, such as mass transformation and
Lith, Pascal F. van; Betlem, Ben H.L.; Roffel, Brian
2002-01-01
Hybrid fuzzy-first principles models can be a good alternative if a complete physical model is difficult to derive. These hybrid models consist of a framework of dynamic mass and energy balances, supplemented by fuzzy submodels describing additional equations, such as mass transformation and
Lith, Pascal F. van; Betlem, Ben H.L.; Roffel, Brian
2002-01-01
Hybrid fuzzy-first principles models can be a good alternative if a complete physical model is difficult to derive. These hybrid models consist of a framework of dynamic mass and energy balances, supplemented by fuzzy submodels describing additional equations, such as mass transformation and transfe
Lith, Pascal F. van; Betlem, Ben H.L.; Roffel, Brian
2002-01-01
Hybrid fuzzy-first principles models can be a good alternative if a complete physical model is difficult to derive. These hybrid models consist of a framework of dynamic mass and energy balances, supplemented by fuzzy submodels describing additional equations, such as mass transformation and transfe
Institute of Scientific and Technical Information of China (English)
褚菲; 马小平; 王福利; 贾润达
2015-01-01
A novel approach for constructing robust Mamdani fuzzy system was proposed, which consisted of an efficiency robust estimator (partial robust M-regression, PRM) in the parameter learning phase of the initial fuzzy system, and an improved subtractive clustering algorithm in the fuzzy-rule-selecting phase. The weights obtained in PRM, which gives protection against noise and outliers, were incorporated into the potential measure of the subtractive cluster algorithm to enhance the robustness of the fuzzy rule cluster process, and a compact Mamdani-type fuzzy system was established after the parameters in the consequent parts of rules were re-estimated by partial least squares (PLS). The main characteristics of the new approach were its simplicity and ability to construct fuzzy system fast and robustly. Simulation and experiment results show that the proposed approach can achieve satisfactory results in various kinds of data domains with noise and outliers. Compared with D-SVD and ARRBFN, the proposed approach yields much fewer rules and less RMSE values.
Information Clustering Based on Fuzzy Multisets.
Miyamoto, Sadaaki
2003-01-01
Proposes a fuzzy multiset model for information clustering with application to information retrieval on the World Wide Web. Highlights include search engines; term clustering; document clustering; algorithms for calculating cluster centers; theoretical properties concerning clustering algorithms; and examples to show how the algorithms work.…
A Genetic Algorithm That Exchanges Neighboring Centers for Fuzzy c-Means Clustering
Chahine, Firas Safwan
2012-01-01
Clustering algorithms are widely used in pattern recognition and data mining applications. Due to their computational efficiency, partitional clustering algorithms are better suited for applications with large datasets than hierarchical clustering algorithms. K-means is among the most popular partitional clustering algorithm, but has a major…
A Genetic Algorithm That Exchanges Neighboring Centers for Fuzzy c-Means Clustering
Chahine, Firas Safwan
2012-01-01
Clustering algorithms are widely used in pattern recognition and data mining applications. Due to their computational efficiency, partitional clustering algorithms are better suited for applications with large datasets than hierarchical clustering algorithms. K-means is among the most popular partitional clustering algorithm, but has a major…
Yuan, Y.
2014-04-28
Energy is a major factor in designing wireless sensor networks (WSNs). In particular, in the real world, battery energy is limited; thus the effective improvement of the energy becomes the key of the routing protocols. Besides, the sensor nodes are always deployed far away from the base station and the transmission energy consumption is index times increasing with the increase of distance as well. This paper proposes a new routing method for WSNs to extend the network lifetime using a combination of a clustering algorithm, a fuzzy approach, and an A-star method. The proposal is divided into two steps. Firstly, WSNs are separated into clusters using the Stable Election Protocol (SEP) method. Secondly, the combined methods of fuzzy inference and A-star algorithm are adopted, taking into account the factors such as the remaining power, the minimum hops, and the traffic numbers of nodes. Simulation results demonstrate that the proposed method has significant effectiveness in terms of balancing energy consumption as well as maximizing the network lifetime by comparing the performance of the A-star and fuzzy (AF) approach, cluster and fuzzy (CF)method, cluster and A-star (CA)method, A-star method, and SEP algorithm under the same routing criteria. 2014 Yali Yuan et al.
Directory of Open Access Journals (Sweden)
Lida Pourjafar
2016-07-01
Full Text Available With the advancement of computer technology, computer simulation in the field of education are more realistic and more effective. The definition of simulation is to create a virtual environment that accurately and real experiences to improve the individual. So Simulation Based Training is the ability to improve, replace, create or manage a real experience and training in a virtual mode. Simulation Based Training also provides large amounts of information to learn, so use data mining techniques to process information in the case of education can be very useful. So here we used data mining to examine the impact of simulation-based training. The database created in cooperation with relevant institutions, including 17 features. To study the effect of selected features, LDA method and Pearson's correlation coefficient was used along with genetic algorithm. Then we use fuzzy clustering to produce fuzzy system and improved it using Neural Networks. The results showed that the proposed method with reduced dimensions have 3% better than other methods.
Fuzzy Clustering with Novel Separable Criterion
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Fuzzy clustering has been used widely in pattern recognition, image processing, and data analysis. An improved fuzzy clustering algorithm was developed based on the conventional fuzzy c-means (FCM) to obtain better quality clustering results. The update equations for the membership and the cluster center are derived from the alternating optimization algorithm. Two fuzzy scattering matrices in the objective function assure the compactness between data points and cluster centers, and also strengthen the separation between cluster centers in terms of a novel separable criterion. The clustering algorithm properties are shown to be an improvement over the FCM method's properties. Numerical simulations show that the clustering algorithm gives more accurate clustering results than the FCM method.
Andryani, Diyah Septi; Bustamam, Alhadi; Lestari, Dian
2017-03-01
Clustering aims to classify the different patterns into groups called clusters. In this clustering method, we use n-mers frequency to calculate the distance matrix which is considered more accurate than using the DNA alignment. The clustering results could be used to discover biologically important sub-sections and groups of genes. Many clustering methods have been developed, while hard clustering methods considered less accurate than fuzzy clustering methods, especially if it is used for outliers data. Among fuzzy clustering methods, fuzzy c-means is one the best known for its accuracy and simplicity. Fuzzy c-means clustering uses membership function variable, which refers to how likely the data could be members into a cluster. Fuzzy c-means clustering works using the principle of minimizing the objective function. Parameters of membership function in fuzzy are used as a weighting factor which is also called the fuzzier. In this study we implement hybrid clustering using fuzzy c-means and divisive algorithm which could improve the accuracy of cluster membership compare to traditional partitional approach only. In this study fuzzy c-means is used in the first step to find partition results. Furthermore divisive algorithms will run on the second step to find sub-clusters and dendogram of phylogenetic tree. To find the best number of clusters is determined using the minimum value of Davies Bouldin Index (DBI) of the cluster results. In this research, the results show that the methods introduced in this paper is better than other partitioning methods. Finally, we found 3 clusters with DBI value of 1.126628 at first step of clustering. Moreover, DBI values after implementing the second step of clustering are always producing smaller IDB values compare to the results of using first step clustering only. This condition indicates that the hybrid approach in this study produce better performance of the cluster results, in term its DBI values.
Fuzzy Clustering of Multiple Instance Data
2015-11-30
NO. 0704-0188 3. DATES COVERED (From - To) - UU UU UU UU 10-03-2016 Approved for public release; distribution is unlimited. Fuzzy Clustering of...RETURN YOUR FORM TO THE ABOVE ADDRESS. University of Louisville 2301 S. Third Street Jouett Hall Louisville, KY 40208 -1838 ABSTRACT Fuzzy Clustering ...and identify K target concepts simultaneously. The proposed algorithm, called Fuzzy Clustering of Multiple Instance data (FCMI), is tested and
Neuro-fuzzy system modeling based on automatic fuzzy clustering
Institute of Scientific and Technical Information of China (English)
Yuangang TANG; Fuchun SUN; Zengqi SUN
2005-01-01
A neuro-fuzzy system model based on automatic fuzzy clustering is proposed.A hybrid model identification algorithm is also developed to decide the model structure and model parameters.The algorithm mainly includes three parts:1) Automatic fuzzy C-means (AFCM),which is applied to generate fuzzy rules automatically,and then fix on the size of the neuro-fuzzy network,by which the complexity of system design is reducesd greatly at the price of the fitting capability;2) Recursive least square estimation (RLSE).It is used to update the parameters of Takagi-Sugeno model,which is employed to describe the behavior of the system;3) Gradient descent algorithm is also proposed for the fuzzy values according to the back propagation algorithm of neural network.Finally,modeling the dynamical equation of the two-link manipulator with the proposed approach is illustrated to validate the feasibility of the method.
A physical analogy to fuzzy clustering
DEFF Research Database (Denmark)
Jantzen, Jan
2004-01-01
This tutorial paper provides an interpretation of the membership assignment in the fuzzy clustering algorithm fuzzy c-means. The membership of a data point to several clusters is shown to be analogous to the gravitational forces between bodies of mass. This provides an alternative way to explain...
A physical analogy to fuzzy clustering
DEFF Research Database (Denmark)
Jantzen, Jan
2004-01-01
This tutorial paper provides an interpretation of the membership assignment in the fuzzy clustering algorithm fuzzy c-means. The membership of a data point to several clusters is shown to be analogous to the gravitational forces between bodies of mass. This provides an alternative way to explain ...
Directory of Open Access Journals (Sweden)
E.A. Zanaty
2012-03-01
Full Text Available In this paper, we determine the suitable validity criterion of kernelized fuzzy C-means and kernelized fuzzy C-means with spatial constraints for automatic segmentation of magnetic resonance imaging (MRI. For that; the original Euclidean distance in the FCM is replaced by a Gaussian radial basis function classifier (GRBF and the corresponding algorithms of FCM methods are derived. The derived algorithms are called as the kernelized fuzzy C-means (KFCM and kernelized fuzzy C-means with spatial constraints (SKFCM. These methods are implemented on eighteen indexes as validation to determine whether indexes are capable to acquire the optimal clusters number. The performance of segmentation is estimated by applying these methods independently on several datasets to prove which method can give good results and with which indexes. Our test spans various indexes covering the classical and the rather more recent indexes that have enjoyed noticeable success in that field. These indexes are evaluated and compared by applying them on various test images, including synthetic images corrupted with noise of varying levels, and simulated volumetric MRI datasets. Comparative analysis is also presented to show whether the validity index indicates the optimal clustering for our datasets.
Fuzzy Clustering Methods and their Application to Fuzzy Modeling
DEFF Research Database (Denmark)
Kroszynski, Uri; Zhou, Jianjun
1999-01-01
Fuzzy modeling techniques based upon the analysis of measured input/output data sets result in a set of rules that allow to predict system outputs from given inputs. Fuzzy clustering methods for system modeling and identification result in relatively small rule-bases, allowing fast, yet accurate...... prediction of outputs. This article presents an overview of some of the most popular clustering methods, namely Fuzzy Cluster-Means (FCM) and its generalizations to Fuzzy C-Lines and Elliptotypes. The algorithms for computing cluster centers and principal directions from a training data-set are described....... A method to obtain an optimized number of clusters is outlined. Based upon the cluster's characteristics, a behavioural model is formulated in terms of a rule-base and an inference engine. The article reviews several variants for the model formulation. Some limitations of the methods are listed...
Alternative Fuzzy Cluster Segmentation of Remote Sensing Images Based on Adaptive Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
WANG Jing; TANG Jilong; LIU Jibin; REN Chunying; LIU Xiangnan; FENG Jiang
2009-01-01
Remote sensing image segmentation is the basis of image understanding and analysis. However, the precision and the speed of segmentation can not meet the need of image analysis, due to strong uncertainty and rich texture details of remote sensing images. We proposed a new segmentation method based on Adaptive Genetic Algorithm (AGA) and Alternative Fuzzy C-Means (AFCM). Segmentation thresholds were identified by AGA. Then the image was segmented by AFCM. The results indicate that the precision and the speed of segmentation have been greatly increased, and the accuracy of threshold selection is much higher compared with traditional Otsu and Fuzzy C-Means (FCM) segmentation methods. The segmentation results also show that multi-thresholds segmentation has been achieved by combining AGA with AFCM.
FINDCLUS : Fuzzy INdividual Differences CLUStering
Giordani, Paolo; Kiers, Henk A. L.
ADditive CLUStering (ADCLUS) is a tool for overlapping clustering of two-way proximity matrices (objects x objects). In Simple Additive Fuzzy Clustering (SAFC), a variant of ADCLUS is introduced providing a fuzzy partition of the objects, that is the objects belong to the clusters with the so-called
Taste Identification of Tea Through a Fuzzy Neural Network Based on Fuzzy C-means Clustering
Institute of Scientific and Technical Information of China (English)
ZHENG Yan; ZHOU Chun-guang
2003-01-01
In this paper, we present a fuzzy neural network model based on Fuzzy C-Means (FCM) clustering algorithm to realize the taste identification of tea. The proposed method can acquire the fuzzy subset and its membership function in an automatic way with the aid of FCM clustering algorithm. Moreover, we improve the fuzzy weighted inference approach. The proposed model is illustrated with the simulation of taste identification of tea.
A FUZZY CLOPE ALGORITHM AND ITS OPTIMAL PARAMETER CHOICE
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Among the available clustering algorithms in data mining, the CLOPE algorithm attracts much more attention with its high speed and good performance. However, the proper choice of some parameters in the CLOPE algorithm directly affects the validity of the clustering results, which is still an open issue. For this purpose, this paper proposes a fuzzy CLOPE algorithm, and presents a method for the optimal parameter choice by defining a modified partition fuzzy degree as a clustering validity function. The experimental results with real data set illustrate the effectiveness of the proposed fuzzy CLOPE algorithm and optimal parameter choice method based on the modified partition fuzzy degree.
Web Fuzzy Clustering and a Case Study
Institute of Scientific and Technical Information of China (English)
LIU Mao-fu; HE Jing; HE Yan-xiang; HU Hui-jun
2004-01-01
We combine the web usage mining and fuzzy clustering and give the concept of web fuzzy clustering, and then put forward the web fuzzy clustering processing model which is discussed in detail. Web fuzzy clustering can be used in the web users clustering and web pages clustering. In the end, a case study is given and the result has proved the feasibility of using web fuzzy clustering in web pages clustering.
Directory of Open Access Journals (Sweden)
Dmitri A. Viattchenin
2009-06-01
Full Text Available This paper describes a modification of a possibilistic clustering method based on the concept of allotment among fuzzy clusters. Basic ideas of the method are considered and the concept of a principal allotment among fuzzy clusters is introduced. The paper provides the description of the plan of the algorithm for detection principal allotment. An analysis of experimental results of the proposed algorithm’s application to the Tamura’s portrait data in comparison with the basic version of the algorithm and with the NERFCM-algorithm is carried out. A methodology of the algorithm’s application to the dimensionality reduction problem is outlined and the application of the methodology is illustrated on the example of Anderson’s Iris data in comparison with the result of principal component analysis. Preliminary conclusions are formulated also.
Fuzzy Clustering Using the Convex Hull as Geometrical Model
Directory of Open Access Journals (Sweden)
Luca Liparulo
2015-01-01
Full Text Available A new approach to fuzzy clustering is proposed in this paper. It aims to relax some constraints imposed by known algorithms using a generalized geometrical model for clusters that is based on the convex hull computation. A method is also proposed in order to determine suitable membership functions and hence to represent fuzzy clusters based on the adopted geometrical model. The convex hull is not only used at the end of clustering analysis for the geometric data interpretation but also used during the fuzzy data partitioning within an online sequential procedure in order to calculate the membership function. Consequently, a pure fuzzy clustering algorithm is obtained where clusters are fitted to the data distribution by means of the fuzzy membership of patterns to each cluster. The numerical results reported in the paper show the validity and the efficacy of the proposed approach with respect to other well-known clustering algorithms.
An incremental clustering algorithm based on Mahalanobis distance
Aik, Lim Eng; Choon, Tan Wee
2014-12-01
Classical fuzzy c-means clustering algorithm is insufficient to cluster non-spherical or elliptical distributed datasets. The paper replaces classical fuzzy c-means clustering euclidean distance with Mahalanobis distance. It applies Mahalanobis distance to incremental learning for its merits. A Mahalanobis distance based fuzzy incremental clustering learning algorithm is proposed. Experimental results show the algorithm is an effective remedy for the defect in fuzzy c-means algorithm but also increase training accuracy.
关于切换回归的集成模糊聚类算法 GFC%An Integrated Fuzzy Clustering Algorithm GFC for Switching Regressions
Institute of Scientific and Technical Information of China (English)
王士同; 江海峰; 陆宏钧
2002-01-01
已经有多个方法可用于解决切换回归问题.根据所提出的基于Newton引力定理的引力聚类算法GC,结合模糊聚类算法,进一步提出了新的集成模糊聚类算法 GFC.理论分析表明GFC 能收敛到局部最小.实验结果表明GFC在解决切换回归问题时,比标准模糊聚类算法更有效,特别在收敛速度方面.%In order to solve switching regression problems, many approaches have been investigated. In this paper, anintegrated fuzzy clustering algorithm GFC that combines gravity-based clustering algorithm GC with fuzzy clustering is presented. GC, as a new hard clustering algorithm presented here, is based on the well-known Newton's Gravity Law. The theoretic analysis shows that GFC can conve rge to a local minimum of the object function. Experimental results show that GFC for switching regression problems has better performance than standard fuzzy clustering algorithms, especially in terms of convergence speed.
Fuzzy clustering of mechanisms
Indian Academy of Sciences (India)
Amitabha Ghosh; Dilip Kumar Pratihar; M V V Amarnath; Guenter Dittrich; Jorg Mueller
2012-10-01
During the course of development of Mechanical Engineering, a large number of mechanisms (that is, linkages to perform various types of tasks) have been conceived and developed. Quite a few atlases and catalogues were prepared by the designers of machines and mechanical systems. However, often it is felt that a clustering technique for handling the list of large number of mechanisms can be very useful,if it is developed based on a scientiﬁc principle. In this paper, it has been shown that the concept of fuzzy sets can be conveniently used for this purpose, if an adequate number of properly chosen attributes (also called characteristics) are identiﬁed. Using two clustering techniques, the mechanisms have been classiﬁed in the present work and in future, it may be extended to develop an expert system, which can automate type synthesis phase of mechanical design. To the best of the authors’ knowledge, this type of clustering of mechanisms has not been attempted before. Thus, this is the ﬁrst attempt to cluster the mechanisms based on some quantitative measures. It may help the engineers to carry out type synthesis of the mechanisms.
FUZZY CLUSTERING ALGORITHMS FOR WEB PAGES AND CUSTOMER SEGMENTS%Web页面和客户群体的模糊聚类算法
Institute of Scientific and Technical Information of China (English)
宋擒豹; 沈钧毅
2001-01-01
Web log mining is broadly used in E-commerce and personalizationof the Web. In this paper, the fuzzy clustering algorithms for Web pages and customers is presented. First, the fuzzy sets of Web page and customer are setup separately according to the hitting information of customers. Second, the fuzzy similarity matrices ave constructed on the basis of the fuzzy sets and the Max-Min similarity measure scheme. Finally, Web page clusters and tustomer segments are abstracted directly from the corresponding fuzzy similarity matrix. Experiments show the effectiveness of the algorithm.%web日志挖掘在电子商务和个性化web等方面有着广泛的应用.文章介绍了一种web页面和客户群体的模糊聚类算法.在该算法中，首先根据客户对Web站点的浏览情况分别建立Web页面和客户的模糊集，在此基础上根据Max—Min模糊相似性度量规则构造相应的模糊相似矩阵，然后根据模糊相似矩阵直接进行聚类.实验结果表明该算法是有效的.
改进的模糊聚类遥感影像分割%Improved Fuzzy Clustering Image Segmentation Algorithm for Remote Sensing Images
Institute of Scientific and Technical Information of China (English)
高国勇
2015-01-01
聚类是数据挖掘的重要分支之一，引入模糊理论的模糊聚类分析为显示数据提供了模糊处理能力，在许多领域被广泛应用。本文应用考虑邻域关系的约束模糊C均值（ Fuzzy C-Means with Constrains ， FCM＿S）算法，将邻域像素引入到目标函数中，进而有效地利用邻域像素信息，提高分割精度。本文应用FCM＿S算法对模拟彩色纹理图像进行分割，计算其混淆矩阵，定性定量地与FCM算法进行对比分析，证明了该算法的鲁棒性。%Clustering is an important branch of data mining , fuzzy theory of fuzzy clustering analysis provides a fuzzy display data pro-cessing capability , is widely used in many fields .In the paper , considering constraints neighborhood relations Fuzzy C -means ( Fuzz-y C-means with Constrains , FCM_S) algorithm will be introduced to the neighboring pixels objective function , thus the effective use of field-pixel information to improve classification accuracy .In this paper , FCM_S algorithm to simulate color texture image classifi-cation, calculate the confusion matrix , qualitative and quantitative comparison with FCM algorithm analysis to prove the robustness of algorithm.
Fuzzy Logic Connectivity in Semiconductor Defect Clustering
Energy Technology Data Exchange (ETDEWEB)
Gleason, S.S.; Kamowski, T.P.; Tobin, K.W.
1999-01-24
In joining defects on semiconductor wafer maps into clusters, it is common for defects caused by different sources to overlap. Simple morphological image processing tends to either join too many unrelated defects together or not enough together. Expert semiconductor fabrication engineers have demonstrated that they can easily group clusters of defects from a common manufacturing problem source into a single signature. Capturing this thought process is ideally suited for fuzzy logic. A system of rules was developed to join disconnected clusters based on properties such as elongation, orientation, and distance. The clusters are evaluated on a pair-wise basis using the fuzzy rules and are joined or not joined based on a defuzzification and threshold. The system continuously re-evaluates the clusters under consideration as their fuzzy memberships change with each joining action. The fuzzy membership functions for each pair-wise feature, the techniques used to measure the features, and methods for improving the speed of the system are all developed. Examples of the process are shown using real-world semiconductor wafer maps obtained from chip manufacturers. The algorithm is utilized in the Spatial Signature Analyzer (SSA) software, a joint development project between Oak Ridge National Lab (ORNL) and SEMATECH.
Fuzzy Logic Connectivity in Semiconductor Defect Clustering
Energy Technology Data Exchange (ETDEWEB)
Gleason, S.S.; Kamowski, T.P.; Tobin, K.W.
1999-01-24
In joining defects on semiconductor wafer maps into clusters, it is common for defects caused by different sources to overlap. Simple morphological image processing tends to either join too many unrelated defects together or not enough together. Expert semiconductor fabrication engineers have demonstrated that they can easily group clusters of defects from a common manufacturing problem source into a single signature. Capturing this thought process is ideally suited for fuzzy logic. A system of rules was developed to join disconnected clusters based on properties such as elongation, orientation, and distance. The clusters are evaluated on a pair-wise basis using the fuzzy rules and are joined or not joined based on a defuzzification and threshold. The system continuously re-evaluates the clusters under consideration as their fuzzy memberships change with each joining action. The fuzzy membership functions for each pair-wise feature, the techniques used to measure the features, and methods for improving the speed of the system are all developed. Examples of the process are shown using real-world semiconductor wafer maps obtained from chip manufacturers. The algorithm is utilized in the Spatial Signature Analyzer (SSA) software, a joint development project between Oak Ridge National Lab (ORNL) and SEMATECH.
Fuzzy Document Clustering Approach using WordNet Lexical Categories
Gharib, Tarek F.; Fouad, Mohammed M.; Aref, Mostafa M.
Text mining refers generally to the process of extracting interesting information and knowledge from unstructured text. This area is growing rapidly mainly because of the strong need for analysing the huge and large amount of textual data that reside on internal file systems and the Web. Text document clustering provides an effective navigation mechanism to organize this large amount of data by grouping their documents into a small number of meaningful classes. In this paper we proposed a fuzzy text document clustering approach using WordNet lexical categories and Fuzzy c-Means algorithm. Some experiments are performed to compare efficiency of the proposed approach with the recently reported approaches. Experimental results show that Fuzzy clustering leads to great performance results. Fuzzy c-means algorithm overcomes other classical clustering algorithms like k-means and bisecting k-means in both clustering quality and running time efficiency.
Mining fuzzy conceptual clusters and constructing the fuzzy conceptual frame lattices
Narang, Vibhu; Kumar, Naveen
2004-04-01
The key idea here is to use formal concept analysis and fuzzy membership criterion to partition the data space into clusters and provide knowledge through fuzzy lattices. The procedures, written here, are regarded as mapping or transform of the original space (samples) onto concepts. The mapping is further given the fuzzy membership criteria for clustering from which the clustered concepts of various degrees are found. Bucket hashing measure has been used as a measure of similarity in the proposed algorithm. The concepts are evaluated on the basis of this criterion and then they are clustered. The intuitive appeal of this approach lies in the fact that once the concepts are clustered, the data analyst is equipped with the concept measure as well as the identification of the bridging points. An interactive concept map visualization technique called Fuzzy Conceptual Frame Lattice or Fuzzy Concept Lattices is presented for user-guided knowledge discovery from the knowledge base.
Genetic fuzzy clustering algorithm for point cloud data segmentation%应用遗传模糊聚类实现点云数据区域分割
Institute of Scientific and Technical Information of China (English)
李海伦; 黎荣; 丁国富; 葛源坤
2012-01-01
为了准确地实现点云数据的区域分割,将基于遗传算法的模糊聚类算法应用于逆向工程中的点云数据区域分割中.首先估算出法矢量、高斯曲率和平均曲率,并与坐标一起组成八维特征向量,用加权距离代替欧氏距离,然后通过遗传算法获得全局最优解的近似解；最后将近似解作为模糊聚类的初始解进行迭代,实现点云数据的区域分割,从而避免传统FCM算法的局部性和对初始解的敏感性,减少了迭代次数.以汽车钣金件为例,证明了应用遗传模糊聚类实现点云数据区域分割的有效性,并验证了该方法能快速、准确地实现点云数据的区域分割.%In order to realize point cloud data segmentation accurately, this paper applied genetic fuzzy clustering algorithm to the point cloud data segmentation in reverse engineering. First, it estimated the normal vector, Gaussian curvature and mean curvature, together with the coordinates of the eight-dimensional feature vector component, using weight distance replaced the Euclidean distance. Through the genetic algorithm, it obtained the approximate solution of the global optimal solution. Finally it used the approximate solution as the initial solutions of fuzzy clustering iteration achieved the point cloud data region segmentation , therefore, avoided the locality and sensitiveness of the initial condition of fuzzy clustering algorithm, at the same time, it reduced the number of iterations. Taking car sheet metal for an example proves the validation of genetic fuzzy clustering algorithm applied to the point cloud data segmentation. And point cloud data can be segmented fast and accurately by this algorithm.
Supplier Segmentation using Fuzzy Linguistic Preference Relations and Fuzzy Clustering
Directory of Open Access Journals (Sweden)
Pegah Sagheb Haghighi
2014-04-01
Full Text Available In an environment characterized by its competitiveness, managing and monitoring relationships with suppliers are of the essence. Supplier management includes supplier segmentation. Existing literature demonstrates that suppliers are mostly segmented by computing their aggregated scores, without taking each supplier’s criterion value into account. The principle aim of this paper is to propose a supplier segmentation method that compares each supplier’s criterion value with exactly the same criterion of other suppliers. The Fuzzy Linguistic Preference Relations (LinPreRa based Analytic Hierarchy Process (AHP is first used to find the weight of each criterion. Then, Fuzzy c-means algorithm is employed to cluster suppliers based on their membership degrees. The obtained results show that the proposed method enhances the quality of the previous findings.
AN ALGORITHM OF TEST FOR FUZZY CODES
Institute of Scientific and Technical Information of China (English)
MoZhiwen; PenJiayin
2001-01-01
Abstract. How to verify that a given fuzzy set A∈F(X ) is a fuzzy code? In this paper, an al-gorithm of test has been introduced and studied with the example of test. The measure notionfor a fuzzy code and a precise formulation of fuzzy codes and words have been discussed.
Novel Cluster Validity Index for FCM Algorithm
Institute of Scientific and Technical Information of China (English)
Jian Yu; Cui-Xia Li
2006-01-01
How to determine an appropriate number of clusters is very important when implementing a specific clustering algorithm, like c-means, fuzzy c-means (FCM). In the literature, most cluster validity indices are originated from partition or geometrical property of the data set. In this paper, the authors developed a novel cluster validity index for FCM, based on the optimality test of FCM. Unlike the previous cluster validity indices, this novel cluster validity index is inherent in FCM itself. Comparison experiments show that the stability index can be used as cluster validity index for the fuzzy c-means.
Institute of Scientific and Technical Information of China (English)
朱群雄; 耿志强
2005-01-01
Overmany alarms of modern chemical process give the operators many difficulties to decision and diagnosis. In order to ensure safe production and process operating, management and optimization of alarm information are challenge work that must be confronted. A new process alarm management method based on fuzzy clusteringranking algorithm is proposed. The fuzzy clustering algorithm is used to cluster rationally the process variables,and difference driving decision algorithm ranks different clusters and process parameters in every cluster. The alarm signal of higher rank is handled preferentially to manage effectively alarms and avoid blind operation. The validity of proposed algorithm and solution is verified by the practical application of ethylene cracking furnace system. It is an effective and dependable alarm management method to improve operating safety in industrial process.
A new fuzzy edge detection algorithm
Institute of Scientific and Technical Information of China (English)
SunWei; XiaLiangzheng
2003-01-01
Based upon the maximum entropy theorem of information theory, a novel fuzzy approach for edge detection is presented. Firsdy, a definition of fuzzy partition entropy is proposed after introducing the concepts of fuzzy probability and fuzzy partition. The relation of the probability partition and the fuzzy c-partition of the image gradient are used in the algorithm. Secondly, based on the conditional probabilities and the fuzzy partition, the optimal thresholding is searched adaptively through the maximum fuzzy entropy principle, and then the edge image is obtained. Lastly, an edge-enhancing procedure is executed on the edge image. The experimental results show that the proposed approach performs well.
Kernel Generalized Noise Clustering Algorithm
Institute of Scientific and Technical Information of China (English)
WU Xiao-hong; ZHOU Jian-jiang
2007-01-01
To deal with the nonlinear separable problem, the generalized noise clustering (GNC) algorithm is extended to a kernel generalized noise clustering (KGNC) model. Different from the fuzzy c-means (FCM) model and the GNC model which are based on Euclidean distance, the presented model is based on kernel-induced distance by using kernel method. By kernel method the input data are nonlinearly and implicitly mapped into a high-dimensional feature space, where the nonlinear pattern appears linear and the GNC algorithm is performed. It is unnecessary to calculate in high-dimensional feature space because the kernel function can do itjust in input space. The effectiveness of the proposed algorithm is verified by experiments on three data sets. It is concluded that the KGNC algorithm has better clustering accuracy than FCM and GNC in clustering data sets containing noisy data.
Energy Technology Data Exchange (ETDEWEB)
Machado, Marcelo Dornellas; Sacco, Wagner Figueiredo; Schirru, Roberto [Universidade Federal, Rio de Janeiro, RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia. Programa de Engenharia Nuclear
2000-07-01
Genetic Algorithms (GAs) are biologically motivated adaptive systems which have been used, with good results, in function optimization. However, traditional GAs rapidly push an artificial population toward convergence. That is, all individuals in the population soon become nearly identical. Niching Methods allow genetic algorithms to maintain a population of diverse individuals. GAs that incorporate these methods are capable of locating multiple, optimal solutions within a single population. The purpose of this study is to introduce a new niching technique based on the fuzzy clustering method FCM, bearing in mind its eventual application in nuclear reactor related problems, specially the nuclear reactor core reload one, which has multiple solutions. tests are performed using widely known test functions and their results show that the new method is quite promising, specially to a future application in real world problems like the nuclear reactor core reload. (author)
EFFICIENT SUBSPACE CLUSTERING FOR HIGHER DIMENSIONAL DATA USING FUZZY ENTROPY
Institute of Scientific and Technical Information of China (English)
C.PALANISAMY; S.SELVAN
2009-01-01
In this paper we propose a novel method for identifying relevant subspaces using fuzzy entropy and perform clustering. This measure discriminates the real distribution better by using membership functions for measuring class match degrees. Hence the fuzzy entropy reflects more information in the actual disbution of patterns in the subspaces. We use a heuristic procedure based on the silhouette criterion to find the number of clusters. The presented theories and algorithms are evaluated through experiments on a collection of benchmark data sets. Empirical results have shown its favorable performance in comparison with several other clustering algorithms.
Genetic Algorithm Optimization for Determining Fuzzy Measures from Fuzzy Data
Directory of Open Access Journals (Sweden)
Chen Li
2013-01-01
Full Text Available Fuzzy measures and fuzzy integrals have been successfully used in many real applications. How to determine fuzzy measures is a very difficult problem in these applications. Though there have existed some methodologies for solving this problem, such as genetic algorithms, gradient descent algorithms, neural networks, and particle swarm algorithm, it is hard to say which one is more appropriate and more feasible. Each method has its advantages. Most of the existed works can only deal with the data consisting of classic numbers which may arise limitations in practical applications. It is not reasonable to assume that all data are real data before we elicit them from practical data. Sometimes, fuzzy data may exist, such as in pharmacological, financial and sociological applications. Thus, we make an attempt to determine a more generalized type of general fuzzy measures from fuzzy data by means of genetic algorithms and Choquet integrals. In this paper, we make the first effort to define the σ-λ rules. Furthermore we define and characterize the Choquet integrals of interval-valued functions and fuzzy-number-valued functions based on σ-λ rules. In addition, we design a special genetic algorithm to determine a type of general fuzzy measures from fuzzy data.
Particle identification using clustering algorithms
Wirth, R; Löher, B; Savran, D; Silva, J; Pol, H Álvarez; Gil, D Cortina; Pietras, B; Bloch, T; Kröll, T; Nácher, E; Perea, Á; Tengblad, O; Bendel, M; Dierigl, M; Gernhäuser, R; Bleis, T Le; Winkel, M
2013-01-01
A method that uses fuzzy clustering algorithms to achieve particle identification based on pulse shape analysis is presented. The fuzzy c-means clustering algorithm is used to compute mean (principal) pulse shapes induced by different particle species in an automatic and unsupervised fashion from a mixed set of data. A discrimination amplitude is proposed using these principal pulse shapes to identify the originating particle species of a detector pulse. Since this method does not make any assumptions about the specific features of the pulse shapes, it is very generic and suitable for multiple types of detectors. The method is applied to discriminate between photon- and proton-induced signals in CsI(Tl) scintillator detectors and the results are compared to the well-known integration method.
A novel fuzzy sensor fusion algorithm
Institute of Scientific and Technical Information of China (English)
FU Hua; YANG Yi-kui; MA Ke; LIU Yu-jia
2011-01-01
A novel fusion algorithm was given based on fuzzy similarity and fuzzy integral theory.First,it calculated the fuzzy similarity among a certain sensor's measurement values and the multiple sensors' objective prediction values to determine the importance weight of each sensor and realize multi-sensor data fusion.Then according to the determined importance weight,an intelligent fusion system based on fuzzy integral theory was given,which can solve FEI-DEO and DEI-DEO fusion problems and realize the decision fusion.Simulation results were proved that fuzzy integral algorithm has enhanced the capability of handling the uncertain information and improved the intelligence degrees.
Cluster Analysis and Fuzzy Query in Ship Maintenance and Design
Che, Jianhua; He, Qinming; Zhao, Yinggang; Qian, Feng; Chen, Qi
Cluster analysis and fuzzy query win wide-spread applications in modern intelligent information processing. In allusion to the features of ship maintenance data, a variant of hypergraph-based clustering algorithm, i.e., Correlation Coefficient-based Minimal Spanning Tree(CC-MST), is proposed to analyze the bulky data rooting in ship maintenance process, discovery the unknown rules and help ship maintainers make a decision on various device fault causes. At the same time, revising or renewing an existed design of ship or device maybe necessary to eliminate those device faults. For the sake of offering ship designers some valuable hints, a fuzzy query mechanism is designed to retrieve the useful information from large-scale complicated and reluctant ship technical and testing data. Finally, two experiments based on a real ship device fault statistical dataset validate the flexibility and efficiency of the CC-MST algorithm. A fuzzy query prototype demonstrates the usability of our fuzzy query mechanism.
Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong
2015-01-01
In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands.
Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong
2015-01-01
In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands. PMID:25691896
Yu, Xuelian; Chen, Qian; Gu, Guohua; Qian, Weixian; Xu, Mengxi
2014-11-01
The integration between polarization and intensity images possessing complementary and discriminative information has emerged as a new and important research area. On the basis of the consideration that the resulting image has different clarity and layering requirement for the target and background, we propose a novel fusion method based on non-subsampled Contourlet transform (NSCT) and fuzzy C-means (FCM) segmentation for IR polarization and light intensity images. First, the polarization characteristic image is derived from fusion of the degree of polarization (DOP) and the angle of polarization (AOP) images using local standard variation and abrupt change degree (ACD) combined criteria. Then, the polarization characteristic image is segmented with FCM algorithm. Meanwhile, the two source images are respectively decomposed by NSCT. The regional energy-weighted and similarity measure are adopted to combine the low-frequency sub-band coefficients of the object. The high-frequency sub-band coefficients of the object boundaries are integrated through the maximum selection rule. In addition, the high-frequency sub-band coefficients of internal objects are integrated by utilizing local variation, matching measure and region feature weighting. The weighted average and maximum rules are employed independently in fusing the low-frequency and high-frequency components of the background. Finally, an inverse NSCT operation is accomplished and the final fused image is obtained. The experimental results illustrate that the proposed IR polarization image fusion algorithm can yield an improved performance in terms of the contrast between artificial target and cluttered background and a more detailed representation of the depicted scene.
Logistics Enterprise Evaluation Model Based On Fuzzy Clustering Analysis
Fu, Pei-hua; Yin, Hong-bo
In this thesis, we introduced an evaluation model based on fuzzy cluster algorithm of logistics enterprises. First of all,we present the evaluation index system which contains basic information, management level, technical strength, transport capacity,informatization level, market competition and customer service. We decided the index weight according to the grades, and evaluated integrate ability of the logistics enterprises using fuzzy cluster analysis method. In this thesis, we introduced the system evaluation module and cluster analysis module in detail and described how we achieved these two modules. At last, we gave the result of the system.
Fuzzy sets, rough sets, multisets and clustering
Dahlbom, Anders; Narukawa, Yasuo
2017-01-01
This book is dedicated to Prof. Sadaaki Miyamoto and presents cutting-edge papers in some of the areas in which he contributed. Bringing together contributions by leading researchers in the field, it concretely addresses clustering, multisets, rough sets and fuzzy sets, as well as their applications in areas such as decision-making. The book is divided in four parts, the first of which focuses on clustering and classification. The second part puts the spotlight on multisets, bags, fuzzy bags and other fuzzy extensions, while the third deals with rough sets. Rounding out the coverage, the last part explores fuzzy sets and decision-making.
Directory of Open Access Journals (Sweden)
Yachun Pang
2012-01-01
Full Text Available This paper presents a novel two-step approach that incorporates fuzzy c-means (FCMs clustering and gradient vector flow (GVF snake algorithm for lesions contour segmentation on breast magnetic resonance imaging (BMRI. Manual delineation of the lesions by expert MR radiologists was taken as a reference standard in evaluating the computerized segmentation approach. The proposed algorithm was also compared with the FCMs clustering based method. With a database of 60 mass-like lesions (22 benign and 38 malignant cases, the proposed method demonstrated sufficiently good segmentation performance. The morphological and texture features were extracted and used to classify the benign and malignant lesions based on the proposed computerized segmentation contour and radiologists’ delineation, respectively. Features extracted by the computerized characterization method were employed to differentiate the lesions with an area under the receiver-operating characteristic curve (AUC of 0.968, in comparison with an AUC of 0.914 based on the features extracted from radiologists’ delineation. The proposed method in current study can assist radiologists to delineate and characterize BMRI lesion, such as quantifying morphological and texture features and improving the objectivity and efficiency of BMRI interpretation with a certain clinical value.
Fuzzy Clustering - Principles, Methods and Examples
DEFF Research Database (Denmark)
Kroszynski, Uri; Zhou, Jianjun
1998-01-01
One of the most remarkable advances in the field of identification and control of systems -in particular mechanical systems- whose behaviour can not be described by means of the usual mathematical models, has been achieved by the application of methods of fuzzy theory.In the framework of a study...... about identification of "black-box" properties by analysis of system input/output data sets, we have prepared an introductory note on the principles and the most popular data classification methods used in fuzzy modeling. This introductory note also includes some examples that illustrate the use...... of the methods. The examples were solved by hand and served as a test bench for exploration of the MATLAB capabilities included in the Fuzzy Control Toolbox. The fuzzy clustering methods described include Fuzzy c-means (FCM), Fuzzy c-lines (FCL) and Fuzzy c-elliptotypes (FCE)....
Hybrid fuzzy cluster ensemble framework for tumor clustering from biomolecular data.
Yu, Zhiwen; Chen, Hantao; You, Jane; Han, Guoqiang; Li, Le
2013-01-01
Cancer class discovery using biomolecular data is one of the most important tasks for cancer diagnosis and treatment. Tumor clustering from gene expression data provides a new way to perform cancer class discovery. Most of the existing research works adopt single-clustering algorithms to perform tumor clustering is from biomolecular data that lack robustness, stability, and accuracy. To further improve the performance of tumor clustering from biomolecular data, we introduce the fuzzy theory into the cluster ensemble framework for tumor clustering from biomolecular data, and propose four kinds of hybrid fuzzy cluster ensemble frameworks (HFCEF), named as HFCEF-I, HFCEF-II, HFCEF-III, and HFCEF-IV, respectively, to identify samples that belong to different types of cancers. The difference between HFCEF-I and HFCEF-II is that they adopt different ensemble generator approaches to generate a set of fuzzy matrices in the ensemble. Specifically, HFCEF-I applies the affinity propagation algorithm (AP) to perform clustering on the sample dimension and generates a set of fuzzy matrices in the ensemble based on the fuzzy membership function and base samples selected by AP. HFCEF-II adopts AP to perform clustering on the attribute dimension, generates a set of subspaces, and obtains a set of fuzzy matrices in the ensemble by performing fuzzy c-means on subspaces. Compared with HFCEF-I and HFCEF-II, HFCEF-III and HFCEF-IV consider the characteristics of HFCEF-I and HFCEF-II. HFCEF-III combines HFCEF-I and HFCEF-II in a serial way, while HFCEF-IV integrates HFCEF-I and HFCEF-II in a concurrent way. HFCEFs adopt suitable consensus functions, such as the fuzzy c-means algorithm or the normalized cut algorithm (Ncut), to summarize generated fuzzy matrices, and obtain the final results. The experiments on real data sets from UCI machine learning repository and cancer gene expression profiles illustrate that 1) the proposed hybrid fuzzy cluster ensemble frameworks work well on real
Mining Representative Subset Based on Fuzzy Clustering
Institute of Scientific and Technical Information of China (English)
ZHOU Hongfang; FENG Boqin; L(U) Lintao
2007-01-01
Two new concepts-fuzzy mutuality and average fuzzy entropy are presented. Then based on these concepts, a new algorithm-RSMA (representative subset mining algorithm) is proposed, which can abstract representative subset from massive data.To accelerate the speed of producing representative subset, an improved algorithm-ARSMA(accelerated representative subset mining algorithm) is advanced, which adopt combining putting forward with backward strategies. In this way, the performance of the algorithm is improved. Finally we make experiments on real datasets and evaluate the representative subset. The experiment shows that ARSMA algorithm is more excellent than RandomPick algorithm either on effectiveness or efficiency.
Advances in theory and applications of fuzzy clustering
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
The summarization and evaluation of the advances in fuzzy clustering theory are made in the aspects including the criterion functions, algorithm implementations, validity measurements and applications. Several important directions for a further study and the application prospects are also pointed out.
FICA:fuzzy imperialist competitive algorithm
Institute of Scientific and Technical Information of China (English)
Saeid ARISH; Ali AMIRI; Khadije NOORI
2014-01-01
Despite the success of the imperialist competitive algorithm (ICA) in solving optimization problems, it still suffers from frequently falling into local minima and low convergence speed. In this paper, a fuzzy version of this algorithm is proposed to address these issues. In contrast to the standard version of ICA, in the proposed algorithm, powerful countries are chosen as imperialists in each step;according to a fuzzy membership function, other countries become colonies of all the empires. In ab-sorption policy, based on the fuzzy membership function, colonies move toward the resulting vector of all imperialists. In this algorithm, no empire will be eliminated;instead, during the execution of the algorithm, empires move toward one point. Other steps of the algorithm are similar to the standard ICA. In experiments, the proposed algorithm has been used to solve the real world optimization problems presented for IEEE-CEC 2011 evolutionary algorithm competition. Results of experiments confirm the performance of the algorithm.
Fuzzy Clustering Validity for Spatial Data%空间数据模糊聚类的有效性
Institute of Scientific and Technical Information of China (English)
胡春春; 孟令奎; 史文中
2008-01-01
The validity measurement of fuzzy clustering is a key problem. If clustering is formed, it needs a kind of machine to verify its validity. To make mining more accountable, comprehensible and with a usable spatial pattern, it is necessary to first detect whether the data set has a clustered structure or not before clustering. This paper discusses a detection method for clustered patterns and a fuzzy clustering algorithm, and studies the validity function of the result produced by fuzzy clustering based on two aspects, which reflect the uncertainty of classification during fuzzy partition and spatial location features of spatial data, and proposes a new validity function of fuzzy clustering for spatial data. The experimental result indicates that the new validity function can accurately measure the validity of the results of fuzzy clustering. Especially, for the result of fuzzy clustering of spatial data, it is robust and its classification result is better when compared to other indices.
Self-organization and clustering algorithms
Bezdek, James C.
1991-01-01
Kohonen's feature maps approach to clustering is often likened to the k or c-means clustering algorithms. Here, the author identifies some similarities and differences between the hard and fuzzy c-Means (HCM/FCM) or ISODATA algorithms and Kohonen's self-organizing approach. The author concludes that some differences are significant, but at the same time there may be some important unknown relationships between the two methodologies. Several avenues of research are proposed.
COMPARISON OF PURITY AND ENTROPY OF K-MEANS CLUSTERING AND FUZZY C MEANS CLUSTERING
Directory of Open Access Journals (Sweden)
Satya Chaitanya Sripada
2011-06-01
Full Text Available Clustering is one the main area in data mining literature. There are various algorithms for clustering. The evaluation of the performance isdone by validation measures. The external validation measures are used to measure the extent to which cluster labels affirm with theexternally given class labels. The aim of this paper is to compare the for K-means and Fuzzy C means clustering using the Purity andEntropy. The data used for evaluating the external measures is medical data.
Partitional clustering algorithms
2015-01-01
This book summarizes the state-of-the-art in partitional clustering. Clustering, the unsupervised classification of patterns into groups, is one of the most important tasks in exploratory data analysis. Primary goals of clustering include gaining insight into, classifying, and compressing data. Clustering has a long and rich history that spans a variety of scientific disciplines including anthropology, biology, medicine, psychology, statistics, mathematics, engineering, and computer science. As a result, numerous clustering algorithms have been proposed since the early 1950s. Among these algorithms, partitional (nonhierarchical) ones have found many applications, especially in engineering and computer science. This book provides coverage of consensus clustering, constrained clustering, large scale and/or high dimensional clustering, cluster validity, cluster visualization, and applications of clustering. Examines clustering as it applies to large and/or high-dimensional data sets commonly encountered in reali...
A fuzzy c-means bi-sonar-based Metaheuristic Optimization Algorithm
Directory of Open Access Journals (Sweden)
Koffka Khan
2012-12-01
Full Text Available Fuzzy clustering is an important problem which is the subject of active research in several real world applications. Fuzzy c-means (FCM algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. Fuzzy clustering methods allow the objects to belong to several clusters simultaneously, with different degrees of membership. Objects on the boundaries between several classes are not forced to fully belong to one of the classes, but rather are assigned membership degrees between 0 and 1 indicating their partial membership. However FCM is sensitive to initialization and is easily trapped in local optima. Bi-sonar optimization (BSO is a stochastic global Metaheuristic optimization tool and is a relatively new algorithm. In this paper a hybrid fuzzy clustering method FCB based on FCM and BSO is proposed which makes use of the merits of both algorithms. Experimental results show that this proposed method is efficient and reveals encouraging results.
Fuzzy Object Skeletonization: Theory, Algorithms, and Applications.
K Saha, Punam; Jin, Dakai; Liu, Yinxiao; E Christensen, Gary; Chen, Cheng
2017-08-10
Skeletonization offers a compact representation of an object while preserving important topological and geometrical features. Literature on skeletonization of binary objects is quite mature. However, challenges involved with skeletonization of fuzzy objects are mostly unanswered. This paper presents a new theory and algorithm of skeletonization for fuzzy objects, evaluates its performance, and demonstrates its applications. A formulation of fuzzy grassfire propagation is introduced; its relationships with fuzzy distance functions, level sets, and geodesics are discussed; and new results are presented. A notion of collision-impact of fire-fronts at skeletal points is introduced, and its role in filtering noisy skeletal points is demonstrated. A fuzzy object skeletonization algorithm is developed using new ideas of surface- and curve-skeletal voxels, digital collision-impact, and continuity of skeletal surfaces. A skeletal noise pruning algorithm is presented using branch-level significance. Accuracy and robustness of the new algorithm are examined on computer-generated phantoms and micro- and conventional CT imaging of trabecular bone specimens. An application of fuzzy object skeletonization to compute structure-width at a low image resolution is demonstrated, and its ability to predict bone strength is examined. Finally, the performance of the new fuzzy object skeletonization algorithm is compared with two binary object skeletonization methods.
Genetic algorithms and fuzzy multiobjective optimization
Sakawa, Masatoshi
2002-01-01
Since the introduction of genetic algorithms in the 1970s, an enormous number of articles together with several significant monographs and books have been published on this methodology. As a result, genetic algorithms have made a major contribution to optimization, adaptation, and learning in a wide variety of unexpected fields. Over the years, many excellent books in genetic algorithm optimization have been published; however, they focus mainly on single-objective discrete or other hard optimization problems under certainty. There appears to be no book that is designed to present genetic algorithms for solving not only single-objective but also fuzzy and multiobjective optimization problems in a unified way. Genetic Algorithms And Fuzzy Multiobjective Optimization introduces the latest advances in the field of genetic algorithm optimization for 0-1 programming, integer programming, nonconvex programming, and job-shop scheduling problems under multiobjectiveness and fuzziness. In addition, the book treats a w...
A HYBRID FIREFLY ALGORITHM WITH FUZZY-C MEAN ALGORITHM FOR MRI BRAIN SEGMENTATION
Directory of Open Access Journals (Sweden)
Mutasem K. Alsmadi
2014-01-01
Full Text Available Image processing is one of the essential tasks to extract suspicious region and robust features from the Magnetic Resonance Imaging (MRI. A numbers of the segmentation algorithms were developed in order to satisfy and increasing the accuracy of brain tumor detection. In the medical image processing brain image segmentation is considered as a complex and challenging part. Fuzzy c-means is unsupervised method that has been implemented for clustering of the MRI and different purposes such as recognition of the pattern of interest and image segmentation. However; fuzzy c-means algorithm still suffers many drawbacks, such as low convergence rate, getting stuck in the local minima and vulnerable to initialization sensitivity. Firefly algorithm is a new population-based optimization method that has been used successfully for solving many complex problems. This paper proposed a new dynamic and intelligent clustering method for brain tumor segmentation using the hybridization of Firefly Algorithm (FA with Fuzzy C-Means algorithm (FCM. In order to automatically segment MRI brain images and improve the capability of the FCM to automatically elicit the proper number and location of cluster centres and the number of pixels in each cluster in the abnormal (multiple sclerosis lesions MRI images. The experimental results proved the effectiveness of the proposed FAFCM in enhancing the performance of the traditional FCM clustering. Moreover; the superiority of the FAFCM with other state-of-the-art segmentation methods is shown qualitatively and quantitatively. Conclusion: A novel efficient and reliable clustering algorithm presented in this work, which is called FAFCM based on the hybridization of the firefly algorithm with fuzzy c-mean clustering algorithm. Automatically; the hybridized algorithm has the capability to cluster and segment MRI brain images.
A Novel Pixon-Based Image Segmentation Process Using Fuzzy Filtering and Fuzzy C-mean Algorithm
DEFF Research Database (Denmark)
Nadernejad, Ehsan; Barari, Amin
2011-01-01
for image segmentation. The key idea is to create a pixon model by combining fuzzy filtering as a kernel function and a fuzzy c-means clustering algorithm for image segmentation. Use of fuzzy filters reduces noise and slightly smoothes the image. Use of the proposed pixon model prevented image over......Image segmentation, which is an important stage of many image processing algorithms, is the process of partitioning an image into nonintersecting regions, such that each region is homogeneous and the union of no two adjacent regions is homogeneous. This paper presents a novel pixon-based algorithm...
A Novel Model of IDS Based on Fuzzy Cluster and Immune Principle
Institute of Scientific and Technical Information of China (English)
TAO Xin-min; LIU Fu-rong
2005-01-01
This paper presents a novel intrusion detection model based on fuzzy cluster and immune principle. The original rival penalized competitive learning (RPCL) algorithm is modified in order to address the problem of different variability of variables and correlation between variables, the sensitivity to initial number of clusters is also solved. Especially, we use the extended RPCL algorithm to determine the initial number of clusters in the fuzzy cluster algorithm. The genetic algorithm is used to optimize the radius deviation for the determination of characteristic function of abnormal subspace.
Directory of Open Access Journals (Sweden)
Wangren Qiu
2015-01-01
Full Text Available In view of techniques for constructing high-order fuzzy time series models, there are three types which are based on advanced algorithms, computational method, and grouping the fuzzy logical relationships. The last type of models is easy to be understood by the decision maker who does not know anything about fuzzy set theory or advanced algorithms. To deal with forecasting problems, this paper presented novel high-order fuzz time series models denoted as GTS (M, N based on generalized fuzzy logical relationships and automatic clustering. This paper issued the concept of generalized fuzzy logical relationship and an operation for combining the generalized relationships. Then, the procedure of the proposed model was implemented on forecasting enrollment data at the University of Alabama. To show the considerable outperforming results, the proposed approach was also applied to forecasting the Shanghai Stock Exchange Composite Index. Finally, the effects of parameters M and N, the number of order, and concerned principal fuzzy logical relationships, on the forecasting results were also discussed.
基于半监督模糊聚类的入侵检测%Semi-supervised fuzzy clustering algorithm for intrusion detection
Institute of Scientific and Technical Information of China (English)
杜红乐; 樊景博
2016-01-01
针对网络行为数据中带标签数据收集困难及网络行为数据的异构性,提出了一种基于异构距离和样本密度的半监督模糊聚类算法,并将该算法应用到网络入侵检测中.该方法依据网络行为数据样本的异构性计算样本与类之间的异构距离及各个类的样本密度,利用异构距离和类内样本密度计算样本与类之间的模糊隶属度,用所得隶属度对无标签样本进行加标签处理,并得到相应的分类器.在KDD CUP99数据集上进行仿真实验,结果表明该方法是可行的、高效的.%Because collecting labeled samples is more difficult than collecting unlabeled samples and network data include value attribute and symbol attribute, this paper proposes an improved semi-supervised fuzzy clustering algorithm based on heterogeneous distance and sample density for intrusion detection. The algorithm computes membership with sample den-sity of one class and heterogeneous distance of intrusion detection dataset. Then it computes distance between sample and the center of every class and sets sample belonging to class of min-distance. It makes experiment with KDDCUP99 datas-et, and experimental results show that the method improves the detection accuracy.
Fuzzy nodes recognition based on spectral clustering in complex networks
Ma, Yang; Cheng, Guangquan; Liu, Zhong; Xie, Fuli
2017-01-01
In complex networks, information regarding the nodes is usually incomplete because of the effects of interference, noise, and other factors. This results in parts of the network being blurred and some information having an unknown source. In this paper, a spectral clustering algorithm is used to identify fuzzy nodes and solve network reconstruction problems. By changing the fuzzy degree of placeholders, we achieve various degrees of credibility and accuracy for the restored network. Our approach is verified by experiments using open source datasets and simulated data.
Fuzzy Modeled K-Cluster Quality Mining of Hidden Knowledge for Decision Support
Directory of Open Access Journals (Sweden)
S. Parkash Kumar
2011-01-01
Full Text Available Problem statement: The work presented Fuzzy Modeled K-means Cluster Quality Mining of hidden knowledge for Decision Support. Based on the number of clusters, number of objects in each cluster and its cohesiveness, precision and recall values, the cluster quality metrics is measured. The fuzzy k-means is adapted approach by using heuristic method which iterates the cluster to form an efficient valid cluster. With the obtained data clusters, quality assessment is made by predictive mining using decision tree model. Validation criteria focus on the quality metrics of the institution features for cluster formation and handle efficiently the arbitrary shaped clusters. Approach: The proposed work presented a fuzzy k-means cluster algorithm in the formation of student, faculty and infrastructural clusters based on the performance, skill set and facilitation availability respectively. The knowledge hidden among the educational data set is extracted through Fuzzy k-means cluster an unsupervised learning depends on certain initiation values to define the subgroups present in the data set. Results: Based on the features of the dataset and input parameters cluster formation vary, which motivates the clarification of cluster validity. The results of quality indexed fuzzy k-means shows better cluster validation compared to that of traditional k-family algorithm. Conclusion: The experimental results of cluster validation scheme confirm the reliability of validity index showing that it performs better than other k-family clusters.
Institute of Scientific and Technical Information of China (English)
孙延维; 彭智明; 李健波
2015-01-01
针对现有基于改进的K-means模糊聚类的社区发现算法(k-means algorithm for community structures detection based on fuzzy clustering,NKFCM)执行效率较差的问题,将粒子群算法与模糊聚类算法相结合提出了基于粒子群优化与模糊聚类的社区发现算法(community detection algorithm based on particle swarm optimization and fuzzy clustering,PFCM).该算法首先进行迭代运算,找出初始聚类核心,利用以云模型为运行条件的粒子群优化算法确定最优聚类核心与最佳社区个数,最后利用模糊聚类算法(fuzzy c-means algorithm,FCM)进行具体的社区划分.理论解析与测试结果表明:该算法发现网络社区的准确性较高,且与NKFCM算法相比,PFCM在处理网络数据时执行效率获得了极大地提升.
Institute of Scientific and Technical Information of China (English)
肖强; 何瑞春; 张薇; 马昌喜
2014-01-01
In some big cities,the effect and efficiency are both poor to the taxi carpooling. The taxi route clustering and carpooling identification of passenger and taxi are studied by fuzzy clustering and fuzzy recognition theory. Through randomly generated many groups of taxi and passengers data, it is pointed that taxi carpooling in particular conditions, taxi numbers will decided the passenger’s carpooling success rate, but it is found that unlimited increase taxi sample number will increase carpooling success rate to passengers in case of a fixed number of passengers, the taxi income will be stabilized, it will not increase with rising of taxi number. The results indicate that the algorithm is suitable for us take the carpooling problem of large numbers taxi and could be effective measurement for taxi carpooling.%针对目前部分大城市出租车合乘效果差，合乘效率低等现状，本文采用模糊聚类和模糊识别方法，研究出租车行驶路线模糊聚类，并利用行驶路线、行驶时间和合乘人数创建隶属函数，实现合乘乘客与出租车的合乘模糊识别。通过随机生成的多组出租车出行和合乘乘客样本数据，发现在假定的出租车合乘条件下，出租车样本数量决定了合乘的成功率，但同时也发现，在合乘人数固定的情况下，无限制的增加出租车样本数量会增加合乘乘客的搭载成功率，平均每辆合乘出租车的收入并不会随着样本数量的增大而增大，而是趋于稳定值。仿真结果说明，该算法适合于大样本的出租车合乘问题，是一种可以提高出租车合乘成功率的有效方法。
Clustering Association Rules with Fuzzy Concepts
Steinbrecher, Matthias; Kruse, Rudolf
Association rules constitute a widely accepted technique to identify frequent patterns inside huge volumes of data. Practitioners prefer the straightforward interpretability of rules, however, depending on the nature of the underlying data the number of induced rules can be intractable large. Even reasonably sized result sets may contain a large amount of rules that are uninteresting to the user because they are too general, are already known or do not match other user-related intuitive criteria. We allow the user to model his conception of interestingness by means of linguistic expressions on rule evaluation measures and compound propositions of higher order (i.e., temporal changes of rule properties). Multiple such linguistic concepts can be considered a set of fuzzy patterns (Fuzzy Sets and Systems 28(3):313-331, 1988) and allow for the partition of the initial rule set into fuzzy fragments that contain rules of similar membership to a user’s concept (Höppner et al., Fuzzy Clustering, Wiley, Chichester, 1999; Computational Statistics and Data Analysis 51(1):192-214, 2006; Advances in Fuzzy Clustering and Its Applications, chap. 1, pp. 3-30, Wiley, New York, 2007). With appropriate visualization methods that extent previous rule set visualizations (Foundations of Fuzzy Logic and Soft Computing, Lecture Notes in Computer Science, vol. 4529, pp. 295-303, Springer, Berlin, 2007) we allow the user to instantly assess the matching of his concepts against the rule set.
Mathematical Foundation of Basic Algorithms of Fuzzy Reasoning
Institute of Scientific and Technical Information of China (English)
潘正华
2005-01-01
Algorithm of fuzzy reasoning has been successful applied in fuzzy control, but its theoretical foundation of algorithms has not been thoroughly investigated. In this paper, structure of basic algorithms of fuzzy reasoning was studied, its rationality was discussed from the viewpoint of logic and mathematics, and three theorems were proved. These theorems shows that there always exists a mathematical relation (that is, a bounded real function) between the premises and the conclusion for fuzzy reasoning, and in fact various algorithms of fuzzy reasoning are specific forms of this function. Thus these results show that algorithms of fuzzy reasoning are theoretically reliable.
Water quality assessment for Ulansuhai Lake using fuzzy clustering and pattern recognition
Institute of Scientific and Technical Information of China (English)
2008-01-01
Water quality assessment of lakes is important to determine functional zones of water use. Considering the fuzziness during the partitioning process for lake water quality in an arid area, a multiplex model of fuzzy clustering with pattern recognition was developed by integrating transitive closure method, ISODATA algorithm in fuzzy clustering and fuzzy pattern recognition. The model was applied to partition the Ulansuhai Lake, a typical shallow lake in arid climate zone in the west part of Inner Mongolia, China and grade the condition of water quality divisions. The results showed that the partition well matched the real conditions of the lake, and the method has been proved accurate in the application.
Parallel Wolff Cluster Algorithms
Bae, S.; Ko, S. H.; Coddington, P. D.
The Wolff single-cluster algorithm is the most efficient method known for Monte Carlo simulation of many spin models. Due to the irregular size, shape and position of the Wolff clusters, this method does not easily lend itself to efficient parallel implementation, so that simulations using this method have thus far been confined to workstations and vector machines. Here we present two parallel implementations of this algorithm, and show that one gives fairly good performance on a MIMD parallel computer.
Improved Fuzzy C-Means Algorithm for MR Brain Image Segmentation
Directory of Open Access Journals (Sweden)
P.Vasuda,
2010-08-01
Full Text Available Segmentation is an important aspect of medical image processing, where Clustering approach is widely used in biomedical applications particularly for brain tumor detection in abnormal Magnetic Resonance Images (MRI. Fuzzy clustering using Fuzzy C- Means (FCM algorithm proved to be superior over the other clustering approaches in terms of segmentation efficiency. But the major drawback of the FCM algorithm is the huge computational time required for convergence. Theeffectiveness of the FCM algorithm in terms of computational rate is improved by modifying the cluster center and membership value updation criterion. In this paper, convergence rate is compared between the conventional FCM and the Improved FCM.
A New Fuzzy Adaptive Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
FANG Lei; ZHANG Huan-chun; JING Ya-zhi
2005-01-01
Multiple genetic algorithms (GAs) need a large population size, which will take a long time for evolution.A new fuzzy adaptive GA is proposed in this paper. This algorithm is more effective in global search while keeping the overall population size constant. The simulation results of function optimization show that with the proposed algorithm, the phenomenon of premature convergence can be overcome effectively, and a satisfying optimization result is obtained.
Fuzzy Clustering Method for Web User Based on Pages Classification
Institute of Scientific and Technical Information of China (English)
ZHAN Li-qiang; LIU Da-xin
2004-01-01
A new method for Web users fuzzy clustering based on analysis of user interest characteristic is proposed in this article.The method first defines page fuzzy categories according to the links on the index page of the site, then computes fuzzy degree of cross page through aggregating on data of Web log.After that, by using fuzzy comprehensive evaluation method, the method constructs user interest vectors according to page viewing times and frequency of hits, and derives the fuzzy similarity matrix from the interest vectors for the Web users.Finally, it gets the clustering result through the fuzzy clustering method.The experimental results show the effectiveness of the method.
Registration algorithm for sensor alignment based on stochastic fuzzy neural network
Institute of Scientific and Technical Information of China (English)
Li Jiao; Jing Zhongliang; He Jiaona; Wang An
2005-01-01
Multiple sensor registration is an important link in multi-sensors data fusion. The existed algorithm is all based on the assumption that system errors come from a fixed deviation set. But there are many other factors, which can result system errors. So traditional registration algorithms have limitation. This paper presents a registration algorithm for sensor alignment based on stochastic fuzzy neural network (SNFF), and utilized fuzzy clustering algorithm obtaining the number of fuzzy rules. Finally, the simulative result illuminate that this way could gain a satisfing result.
Fuzzy audit risk modeling algorithm
Directory of Open Access Journals (Sweden)
Zohreh Hajihaa
2011-07-01
Full Text Available Fuzzy logic has created suitable mathematics for making decisions in uncertain environments including professional judgments. One of the situations is to assess auditee risks. During recent years, risk based audit (RBA has been regarded as one of the main tools to fight against fraud. The main issue in RBA is to determine the overall audit risk an auditor accepts, which impact the efficiency of an audit. The primary objective of this research is to redesign the audit risk model (ARM proposed by auditing standards. The proposed model of this paper uses fuzzy inference systems (FIS based on the judgments of audit experts. The implementation of proposed fuzzy technique uses triangular fuzzy numbers to express the inputs and Mamdani method along with center of gravity are incorporated for defuzzification. The proposed model uses three FISs for audit, inherent and control risks, and there are five levels of linguistic variables for outputs. FISs include 25, 25 and 81 rules of if-then respectively and officials of Iranian audit experts confirm all the rules.
Classification of excessive domestic water consumption using Fuzzy Clustering Method
Zairi Zaidi, A.; Rasmani, Khairul A.
2016-08-01
Demand for clean and treated water is increasing all over the world. Therefore it is crucial to conserve water for better use and to avoid unnecessary, excessive consumption or wastage of this natural resource. Classification of excessive domestic water consumption is a difficult task due to the complexity in determining the amount of water usage per activity, especially as the data is known to vary between individuals. In this study, classification of excessive domestic water consumption is carried out using a well-known Fuzzy C-Means (FCM) clustering algorithm. Consumer data containing information on daily, weekly and monthly domestic water usage was employed for the purpose of classification. Using the same dataset, the result produced by the FCM clustering algorithm is compared with the result obtained from a statistical control chart. The finding of this study demonstrates the potential use of the FCM clustering algorithm for the classification of domestic consumer water consumption data.
A Fuzzy Co-Clustering approach for Clickstream Data Pattern
Rathipriya, R
2011-01-01
Web Usage mining is a very important tool to extract the hidden business intelligence data from large databases. The extracted information provides the organizations with the ability to produce results more effectively to improve their businesses and increasing of sales. Co-clustering is a powerful bipartition technique which identifies group of users associated to group of web pages. These associations are quantified to reveal the users' interest in the different web pages' clusters. In this paper, Fuzzy Co-Clustering algorithm is proposed for clickstream data to identify the subset of users of similar navigational behavior /interest over a subset of web pages of a website. Targeting the users group for various promotional activities is an important aspect of marketing practices. Experiments are conducted on real dataset to prove the efficiency of proposed algorithm. The results and findings of this algorithm could be used to enhance the marketing strategy for directing marketing, advertisements for web base...
Schröter, Ingmar; Paasche, Hendik; Dietrich, Peter; Wollschläger, Ute
2014-05-01
Soil moisture is a key variable of the hydrological cycle. For example, it controls partitioning of rainfall into a runoff and an infiltration component and modulating physical, chemical and biological processes within the soil. For a better understanding of these processes, knowledge about the spatio-temporal distribution of soil moisture is indispensable. For the field to the small catchment scale with survey areas up to a few square kilometres, there are numerous new and innovative ground-based and remote sensing technologies available which have great potential to provide temporal information about soil moisture patterns. The aim of this work is to design an optimal soil moisture monitoring program for a low-mountain catchment in central Germany. In a first step, the fuzzy c-means clustering technique (Paasche et al., 2006) was used to identify structure-relevant patterns in a set of different terrain attributes derived from a DEM. Based on these patterns optimal measurement locations were identified to conduct in-situ soil moisture measurements. To consider different wetting and drying states in the catchment, several TDR measurement campaigns were conducted from April to October 2013. The TDR measurements have been integrated with the structure-relevant patterns obtained by the fuzzy cluster analysis to regionally predict soil moisture. In this study, we outline the conceptual framework of this integrative approach and present first results from field measurements. The results of the project are expected to improve the monitoring and understanding of small catchment-scale hydrological processes and to contribute to a better representation of soil moisture dynamics in physically-based, hydrological models operating at the field to the small catchment scale. Reference: Paasche, H., J. Tronicke, K. Holliger, A.G. Green, and H. Maurer (2006): Integration of diverse physical-property models: Subsurface zonation and petrophysical parameter estimation based on fuzzy
Directory of Open Access Journals (Sweden)
Özlem Türkşen
2013-01-01
Full Text Available The solution set of a multi-response experiment is characterized by Pareto solution set. In this paper, the multi-response experiment is dealed in a fuzzy framework. The responses and model parameters are considered as triangular fuzzy numbers which indicate the uncertainty of the data set. Fuzzy least square approach and fuzzy modified NSGA-II (FNSGA-II are used for modeling and optimization, respectively. The obtained fuzzy Pareto solution set is grouped by using fuzzy relational clustering approach. Therefore, it could be easier to choose the alternative solutions to make better decision. A fuzzy response valued real data set is used as an application.
Student academic performance analysis using fuzzy C-means clustering
Rosadi, R.; Akamal; Sudrajat, R.; Kharismawan, B.; Hambali, Y. A.
2017-01-01
Grade Point Average (GPA) is commonly used as an indicator of academic performance. Academic performance evaluations is a basic way to evaluate the progression of student performance, when evaluating student’s academic performance, there are occasion where the student data is grouped especially when the amounts of data is large. Thus, the pattern of data relationship within and among groups can be revealed. Grouping data can be done by using clustering method, where one of the methods is the Fuzzy C-Means algorithm. Furthermore, this algorithm is then applied to a set of student data form the Faculty of Mathematics and Natural Sciences, Padjadjaran University.
Cloud classification from satellite data using a fuzzy sets algorithm: A polar example
Key, J. R.; Maslanik, J. A.; Barry, R. G.
1988-01-01
Where spatial boundaries between phenomena are diffuse, classification methods which construct mutually exclusive clusters seem inappropriate. The Fuzzy c-means (FCM) algorithm assigns each observation to all clusters, with membership values as a function of distance to the cluster center. The FCM algorithm is applied to AVHRR data for the purpose of classifying polar clouds and surfaces. Careful analysis of the fuzzy sets can provide information on which spectral channels are best suited to the classification of particular features, and can help determine likely areas of misclassification. General agreement in the resulting classes and cloud fraction was found between the FCM algorithm, a manual classification, and an unsupervised maximum likelihood classifier.
Cloud classification from satellite data using a fuzzy sets algorithm - A polar example
Key, J. R.; Maslanik, J. A.; Barry, R. G.
1989-01-01
Where spatial boundaries between phenomena are diffuse, classification methods which construct mutually exclusive clusters seem inappropriate. The Fuzzy c-means (FCM) algorithm assigns each observation to all clusters, with membership values as a function of distance to the cluster center. The FCM algorithm is applied to AVHRR data for the purpose of classifying polar clouds and surfaces. Careful analysis of the fuzzy sets can provide information on which spectral channels are best suited to the classification of particular features, and can help determine like areas of misclassification. General agreement in the resulting classes and cloud fraction was found between the FCM algorithm, a manual classification, and an unsupervised maximum likelihood classifier.
Cloud classification from satellite data using a fuzzy sets algorithm - A polar example
Key, J. R.; Maslanik, J. A.; Barry, R. G.
1989-01-01
Where spatial boundaries between phenomena are diffuse, classification methods which construct mutually exclusive clusters seem inappropriate. The Fuzzy c-means (FCM) algorithm assigns each observation to all clusters, with membership values as a function of distance to the cluster center. The FCM algorithm is applied to AVHRR data for the purpose of classifying polar clouds and surfaces. Careful analysis of the fuzzy sets can provide information on which spectral channels are best suited to the classification of particular features, and can help determine like areas of misclassification. General agreement in the resulting classes and cloud fraction was found between the FCM algorithm, a manual classification, and an unsupervised maximum likelihood classifier.
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
A self-organizing fuzzy clustering neural network by combining the self-organizing Kohonen clustering network with the fuzzy theory is proposed.This network model is designed for the effectiveness evaluation of electronic countermeasures,which not only exerts the advantages of the fuzzy theory,but also has a good ability in machine learning and data analysis.The subjective value of sample versus class is computed by the fuzzy computing theory,and the classified results obtained by self-organizing learning of Kohonen neural network are represented on output layer.Meanwhile,the fuzzy competition learning algorithm keeps the similar information between samples and overcomes the disadvantages of neural network which has fewer samples.The simulation result indicates that the proposed algorithm is feasible and effective.
Analysis of protein profiles using fuzzy clustering methods
DEFF Research Database (Denmark)
Karemore, Gopal Raghunath; Ukendt, Sujatha; Rai, Lavanya
clustering methods for their classification followed by various validation measures. The clustering algorithms used for the study were K- means, K- medoid, Fuzzy C-means, Gustafson-Kessel, and Gath-Geva. The results presented in this study conclude that the protein profiles of tissue...... samples recorded by using the HPLC- LIF system and the data analyzed by clustering algorithms quite successfully classifies them as belonging from normal and malignant conditions....
New judging model of fuzzy cluster optimal dividing based on rough sets theory
Institute of Scientific and Technical Information of China (English)
Wang Yun; Liu Qinghong; Mu Yong; Shi Kaiquan
2007-01-01
To investigate the judging problem of optimal dividing matrix among several fuzzy dividing matrices in fuzzy dividing space, correspondingly, which is determined by the various choices of cluster samples in the totality sample space, two algorithms are proposed on the basis of the data analysis method in rough sets theory: information system discrete algorithm (algorithm 1) and samples representatives judging algorithm (algorithm 2).On the principle of the farthest distance, algorithm 1 transforms continuous data into discrete form which could be transacted by rough sets theory.Taking the approximate precision as a criterion, algorithm 2 chooses the sample space with a good representative.Hence, the clustering sample set in inducing and computing optimal dividing matrix can be achieved.Several theorems are proposed to provide strict theoretic foundations for the execution of the algorithm model.An applied example based on the new algorithm model is given, whose result verifies the feasibility of this new algorithm model.
A Novel Pixon-Based Image Segmentation Process Using Fuzzy Filtering and Fuzzy C-mean Algorithm
DEFF Research Database (Denmark)
Nadernejad, E; Barari, Amin
2011-01-01
for image segmentation. The key idea is to create a pixon model by combining fuzzy filtering as a kernel function and a fuzzy c-means clustering algorithm for image segmentation. Use of fuzzy filters reduces noise and slightly smoothes the image. Use of the proposed pixon model prevented image over......Image segmentation, which is an important stage of many image processing algorithms, is the process of partitioning an image into nonintersecting regions, such that each region is homogeneous and the union of no two adjacent regions is homogeneous. This paper presents a novel pixon-based algorithm......-segmentation and produced better experimental results than those obtained with other pixon-based algorithms....
The application of genetic fuzzy clustering in bad data identification
Liu, Yunjing; Gu, Deying
2006-11-01
Power system static state estimation is aimed at providing modern electric control centers with accurate and reliable real-time databases. To this end, not only should the state estimator be able to filter out random observation noise but it should also be able to detect the existence, identify the locations and remove the effects of bad data. Detecting and identifying bad data is very important in state estimation of power system. A new method presented in this paper is fuzzy clustering with genetic search. And simulation data proves that error contamination and submergence can be reduced so that real bad data can be detected and identified. A key advantage of the proposed method is that the clustering is independent of the space distribution of input samples. This method possesses characteristics so faster convergence rate and more exact clustering results than some typical clustering algorithms.
An Efficient Fuzzy Clustering-Based Approach for Intrusion Detection
Nguyen, Huu Hoa; Darmont, Jérôme
2011-01-01
The need to increase accuracy in detecting sophisticated cyber attacks poses a great challenge not only to the research community but also to corporations. So far, many approaches have been proposed to cope with this threat. Among them, data mining has brought on remarkable contributions to the intrusion detection problem. However, the generalization ability of data mining-based methods remains limited, and hence detecting sophisticated attacks remains a tough task. In this thread, we present a novel method based on both clustering and classification for developing an efficient intrusion detection system (IDS). The key idea is to take useful information exploited from fuzzy clustering into account for the process of building an IDS. To this aim, we first present cornerstones to construct additional cluster features for a training set. Then, we come up with an algorithm to generate an IDS based on such cluster features and the original input features. Finally, we experimentally prove that our method outperform...
Li, Bing Nan; Chui, Chee Kong; Chang, Stephen; Ong, S H
2011-01-01
The performance of the level set segmentation is subject to appropriate initialization and optimal configuration of controlling parameters, which require substantial manual intervention. A new fuzzy level set algorithm is proposed in this paper to facilitate medical image segmentation. It is able to directly evolve from the initial segmentation by spatial fuzzy clustering. The controlling parameters of level set evolution are also estimated from the results of fuzzy clustering. Moreover the fuzzy level set algorithm is enhanced with locally regularized evolution. Such improvements facilitate level set manipulation and lead to more robust segmentation. Performance evaluation of the proposed algorithm was carried on medical images from different modalities. The results confirm its effectiveness for medical image segmentation.
Shape Prediction Linear Algorithm Using Fuzzy
Directory of Open Access Journals (Sweden)
Navjot Kaur
2012-10-01
Full Text Available The goal of the proposed method is to develop shape prediction algorithm using fuzzy that is computationally fast and invariant. To predict the overlapping and joined shapes accurately, a method of shape prediction based on erosion and over segmentation is used to estimate values for dependent variables from previously unseen predictor values based on the variation in an underlying learning data set.
Institute of Scientific and Technical Information of China (English)
刘太洪; 赵永雷
2016-01-01
为提高变压器故障诊断准确率，提出了一种基于遗传算法的动态加权模糊C均值聚类算法。该算法使用把聚类中心作为染色体的浮点数的编码方式，染色体长度可变，不同的长度对应于不同的故障聚类数；并使用权值区别不同样本点对故障划分的影响程度。将该算法应用于电力变压器油中溶解气体分析（DGA）数据分析，实现了变压器的故障诊断。经过大量实例分析，并将结果与其他算法进行对比，表明该算法具有较高的诊断精度。%ABSTRACT:In order to improve the correct rate of fault diagnosis of transformer, this paper investigates a dynamic weighted fuzzy c-means clustering algorithm based on genetic algorithm. The algorithm adopts a kind of cluster-center-based floating point encoding mode, in which the variable length chromosomes express cluster prototypes and different length of chromosomes corresponding to different numbers of cluster prototypes;besides,The algorithm utilizes the weights to express the relative degree of the importance of various data in fault partitioning. The algorithm is applied to DGA data analysis, which can accomplish fault diagnosis of the transformer. Examples analysis and comparison results show that the preci-sion of fault diagnosis can be evidently improved.
Baraldi, Andrea; Parmiggiani, Flavio
1996-06-01
cognitive tasks. In this paper, the FLVQ model is critically analyzed in order to stress the meaning of a fuzzy learning mechanism. This study leads to the development of a new NN model, termed the fuzzy competitive/cooperative Kohonen (FCCK) model, which replaces FLVQ. Then, the architectural differences amongst three NN algorithms and the relationships between their fuzzy clustering properties are discussed. These models, which all perform on-line learning, are: (1) SOM; (2) FCCK; and (3) improved neural-gas (INC).
A new fuzzy algorithm for ecological ranking
Directory of Open Access Journals (Sweden)
Alessandro Ferrarini
2011-09-01
Full Text Available Ecological ranking is a prerequisite to many kinds of environmental decisions. It requires a set of 'objects' (e.g., competing sites for species reintroduction, or competing alternatives of environmental management to be evaluated on the basis of multiple weighted criteria, and then ranked from the best to the worst, or vice versa. The resulting ranking is then used to choose the course of an action (e.g., the optimal sites where a species can be reintroduced, or the optimal management scenario for a protected area. In this work, a new tool called FuzRnk is proposed as a modification of classic fuzzy algorithm. FuzRnk, which is freely available upon request from the author, allows for a fuzzy ranking of GIS objects (e.g., landscape patches or zones within protected areas. With respect to classic fuzzy algorithm, FuzRnk introduces two modifications: a criteria can be weighted on the basis of their importance, b not only the best performances, but also the worst ones are considered in the ranking procedure.
Hybrid Genetic Algorithms with Fuzzy Logic Controller
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
In this paper, a new implementation of genetic algorithms (GAs) is developed for the machine scheduling problem, which is abundant among the modern manufacturing systems. The performance measure of early and tardy completion of jobs is very natural as one's aim, which is usually to minimize simultaneously both earliness and tardiness of all jobs. As the problem is NP-hard and no effective algorithms exist, we propose a hybrid genetic algorithms approach to deal with it. We adjust the crossover and mutation probabilities by fuzzy logic controller whereas the hybrid genetic algorithm does not require preliminary experiments to determine probabilities for genetic operators. The experimental results show the effectiveness of the GAs method proposed in the paper.``
量子蚁群模糊聚类算法在图像分割中的应用%Image Segmentation Based on Quantum Ant Colony Fuzzy Clustering Algorithm
Institute of Scientific and Technical Information of China (English)
李积英; 党建武
2013-01-01
Fuzzy C-Means algorithm is dependent on the initial value, resulting in easy to fall into the disadvantage of the local optimum value. A combination of quantum ant colony algorithm and FCM clustering algorithm is put forward. Firstly, the original center and numbers of cluster of the image are determined by using global type, robustness and advantages of fast convergence of quantum ant colony algorithm. Secondly, the obtained results are taken as the initial parameters for FCM clustering algorithm, and then the medical image is divided by using FCM clustering algorithm. It is proved that the method has reduced the dependence of FCM clustering algorithm on initial parameters effectively, overcome the shortcomings of easy falling into the local minimum of both algorithms,and greatly improved dividing speed and accuracy, which is simulated by real experiment.% 针对模糊C-均值算法对初始值的依赖，容易陷入局部最优值的缺点，本文提出将量子蚁群算法与FCM聚类算法结合，首先利用量子蚁群算法的全局性和鲁棒性以及快速收敛的优点确定图像的初始聚类中心和聚类个数，再将所得结果作为FCM聚类算法的初始参数，然后用FCM聚类算法对医学图像进行分割。实验结果表明，该方法有效解决了FCM算法对初始参数的依赖，克服了FCM算法及蚁群算法容易陷入局部极值的的缺点，而且在分割速度和精度上得到了较大提高。
Performance Evaluation of K-Mean and Fuzzy C-Mean Image Segmentation Based Clustering Classifier
Directory of Open Access Journals (Sweden)
Hind R.M Shaaban
2015-12-01
Full Text Available This paper presents Evaluation K-mean and Fuzzy c-mean image segmentation based Clustering classifier. It was followed by thresholding and level set segmentation stages to provide accurate region segment. The proposed stay can get the benefits of the K-means clustering. The performance and evaluation of the given image segmentation approach were evaluated by comparing K-mean and Fuzzy c-mean algorithms in case of accuracy, processing time, Clustering classifier, and Features and accurate performance results. The database consists of 40 images executed by K-mean and Fuzzy c-mean image segmentation based Clustering classifier. The experimental results confirm the effectiveness of the proposed Fuzzy c-mean image segmentation based Clustering classifier. The statistical significance Measures of mean values of Peak signal-to-noise ratio (PSNR and Mean Square Error (MSE and discrepancy are used for Performance Evaluation of K-mean and Fuzzy c-mean image segmentation. The algorithm’s higher accuracy can be found by the increasing number of classified clusters and with Fuzzy c-mean image segmentation.
Outlier rejection fuzzy c-means (ORFCM) algorithm for image segmentation
segmentation, Outlier rejection fuzzy c-means (ORFCM)
2013-01-01
This paper presents a fuzzy clustering-based technique for image segmentation. Many attempts have been put into practice to increase the conventional fuzzy c-means (FCM) performance. In this paper, the sensitivity of the soft membership function of the FCM algorithm to the outlier is considered and the new exponent operator on the Euclidean distance is implemented in the membership function to improve the outlier rejection characteristics of the FCM. The comparative quantitative and qua...
Chemical optimization algorithm for fuzzy controller design
Astudillo, Leslie; Castillo, Oscar
2014-01-01
In this book, a novel optimization method inspired by a paradigm from nature is introduced. The chemical reactions are used as a paradigm to propose an optimization method that simulates these natural processes. The proposed algorithm is described in detail and then a set of typical complex benchmark functions is used to evaluate the performance of the algorithm. Simulation results show that the proposed optimization algorithm can outperform other methods in a set of benchmark functions. This chemical reaction optimization paradigm is also applied to solve the tracking problem for the dynamic model of a unicycle mobile robot by integrating a kinematic and a torque controller based on fuzzy logic theory. Computer simulations are presented confirming that this optimization paradigm is able to outperform other optimization techniques applied to this particular robot application
Fuzzy Control of Chaotic System with Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
FANG Jian-an; GUO Zhao-xia; SHAO Shi-huang
2002-01-01
A novel approach to control the unpredictable behavior of chaotic systems is presented. The control algorithm is based on fuzzy logic control technique combined with genetic algorithm. The use of fuzzy logic allows for the implementation of human "rule-of-thumb" approach to decision making by employing linguistic variables. An improved Genetic Algorithm (GA) is used to learn to optimally select the fuzzy membership functions of the linguistic labels in the condition portion of each rule,and to automatically generate fuzzy control actions under each condition. Simulation results show that such an approach for the control of chaotic systems is both effective and robust.
Recovery Rate of Clustering Algorithms
Li, Fajie; Klette, Reinhard; Wada, T; Huang, F; Lin, S
2009-01-01
This article provides a simple and general way for defining the recovery rate of clustering algorithms using a given family of old clusters for evaluating the performance of the algorithm when calculating a family of new clusters. Under the assumption of dealing with simulated data (i.e., known old
Adaptive fuzzy leader clustering of complex data sets in pattern recognition
Newton, Scott C.; Pemmaraju, Surya; Mitra, Sunanda
1992-01-01
A modular, unsupervised neural network architecture for clustering and classification of complex data sets is presented. The adaptive fuzzy leader clustering (AFLC) architecture is a hybrid neural-fuzzy system that learns on-line in a stable and efficient manner. The initial classification is performed in two stages: a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from fuzzy C-means system equations for the centroids and the membership values. The AFLC algorithm is applied to the Anderson Iris data and laser-luminescent fingerprint image data. It is concluded that the AFLC algorithm successfully classifies features extracted from real data, discrete or continuous.
An improved multilevel fuzzy comprehensive evaluation algorithm for security performance
Institute of Scientific and Technical Information of China (English)
LI Ling-juan; SHEN Ling-tong
2006-01-01
It is of great importance to take various factors into account when evaluating the network security performance.Multilevel fuzzy comprehensive evaluation is a relatively valid method. However, the traditional multilevel fuzzy comprehensive evaluation algorithm relies on the expert's knowledge and experiences excessively, and the result of the evaluation is usually less accurate. In this article, an improved multilevel fuzzy comprehensive evaluation algorithm, based on fuzzy sets core and entropy weight is presented. Furthermore, a multilevel fuzzy comprehensive evaluation model of P2P network security performance has also been designed, and the improved algorithm is used to make an instant computation based on the model. The advantages of the improved algorithm can be embodied in comparison with the traditional evaluation algorithm.
Directory of Open Access Journals (Sweden)
Nour-Eddine El Harchaoui
2013-01-01
Full Text Available The analysis and processing of large data are a challenge for researchers. Several approaches have been used to model these complex data, and they are based on some mathematical theories: fuzzy, probabilistic, possibilistic, and evidence theories. In this work, we propose a new unsupervised classification approach that combines the fuzzy and possibilistic theories; our purpose is to overcome the problems of uncertain data in complex systems. We used the membership function of fuzzy c-means (FCM to initialize the parameters of possibilistic c-means (PCM, in order to solve the problem of coinciding clusters that are generated by PCM and also overcome the weakness of FCM to noise. To validate our approach, we used several validity indexes and we compared them with other conventional classification algorithms: fuzzy c-means, possibilistic c-means, and possibilistic fuzzy c-means. The experiments were realized on different synthetics data sets and real brain MR images.
Genetic Algorithm Tuned Fuzzy Logic for Gliding Return Trajectories
Burchett, Bradley T.
2003-01-01
The problem of designing and flying a trajectory for successful recovery of a reusable launch vehicle is tackled using fuzzy logic control with genetic algorithm optimization. The plant is approximated by a simplified three degree of freedom non-linear model. A baseline trajectory design and guidance algorithm consisting of several Mamdani type fuzzy controllers is tuned using a simple genetic algorithm. Preliminary results show that the performance of the overall system is shown to improve with genetic algorithm tuning.
A heuristic approach to possibilistic clustering algorithms and applications
Viattchenin, Dmitri A
2013-01-01
The present book outlines a new approach to possibilistic clustering in which the sought clustering structure of the set of objects is based directly on the formal definition of fuzzy cluster and the possibilistic memberships are determined directly from the values of the pairwise similarity of objects. The proposed approach can be used for solving different classification problems. Here, some techniques that might be useful at this purpose are outlined, including a methodology for constructing a set of labeled objects for a semi-supervised clustering algorithm, a methodology for reducing analyzed attribute space dimensionality and a methods for asymmetric data processing. Moreover, a technique for constructing a subset of the most appropriate alternatives for a set of weak fuzzy preference relations, which are defined on a universe of alternatives, is described in detail, and a method for rapidly prototyping the Mamdani’s fuzzy inference systems is introduced. This book addresses engineers, scientist...
Fuzzy C-Means Clustering and Energy Efficient Cluster Head Selection for Cooperative Sensor Network
Bhatti, Dost Muhammad Saqib; Saeed, Nasir; Nam, Haewoon
2016-01-01
We propose a novel cluster based cooperative spectrum sensing algorithm to save the wastage of energy, in which clusters are formed using fuzzy c-means (FCM) clustering and a cluster head (CH) is selected based on a sensor’s location within each cluster, its location with respect to fusion center (FC), its signal-to-noise ratio (SNR) and its residual energy. The sensing information of a single sensor is not reliable enough due to shadowing and fading. To overcome these issues, cooperative spectrum sensing schemes were proposed to take advantage of spatial diversity. For cooperative spectrum sensing, all sensors sense the spectrum and report the sensed energy to FC for the final decision. However, it increases the energy consumption of the network when a large number of sensors need to cooperate; in addition to that, the efficiency of the network is also reduced. The proposed algorithm makes the cluster and selects the CHs such that very little amount of network energy is consumed and the highest efficiency of the network is achieved. Using the proposed algorithm maximum probability of detection under an imperfect channel is accomplished with minimum energy consumption as compared to conventional clustering schemes. PMID:27618061
Fuzzy c-Means and Cluster Ensemble with Random Projection for Big Data Clustering
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Mao Ye
2016-01-01
Full Text Available Because of its positive effects on dealing with the curse of dimensionality in big data, random projection for dimensionality reduction has become a popular method recently. In this paper, an academic analysis of influences of random projection on the variability of data set and the dependence of dimensions has been proposed. Together with the theoretical analysis, a new fuzzy c-means (FCM clustering algorithm with random projection has been presented. Empirical results verify that the new algorithm not only preserves the accuracy of original FCM clustering, but also is more efficient than original clustering and clustering with singular value decomposition. At the same time, a new cluster ensemble approach based on FCM clustering with random projection is also proposed. The new aggregation method can efficiently compute the spectral embedding of data with cluster centers based representation which scales linearly with data size. Experimental results reveal the efficiency, effectiveness, and robustness of our algorithm compared to the state-of-the-art methods.
基于Hadoop二阶段并行模糊c-Means聚类算法%HADOOP-BASED TWO-STAGE PARALLEL FUZZY C-MEANS CLUSTERING ALGORITHM
Institute of Scientific and Technical Information of China (English)
胡吉朝; 黄红艳
2016-01-01
Aiming at the problem of too high occupancy of communication time and limited applying value of the algorithm under the mechanism of Mapreduce,we put forward a Hadoop-based two-stage parallel c-Means clustering algorithm to deal with the problem of extra-large data classification.First,we improved the MPI communication management method in Mapreduce mechanism,and used membership management protocol mode to realise the synchronisation of members management and Mapreduce reducing operation.Secondly, we implemented typical individuals group reducing operation instead of global individual reducing operation,and defined the two-stage buffer algorithm.Finally,through the buffer in first stage we further reduced the data amount of Mapreduce operation in second stage,and reduced the negative impact brought about by big data on the algorithm as much as possible.Based on this,we carried out the simulation by using artificial big data test set and KDD CUP 99 invasion test data.Experimental result showed that the algorithm could both guarantee the clustering precision requirement and speed up effectively the operation efficiency of algorithm.%针对Mapreduce机制下算法通信时间占用比过高，实际应用价值受限的情况，提出基于Hadoop二阶段并行c-Means聚类算法用来解决超大数据的分类问题。首先，改进Mapreduce机制下的MPI通信管理方法，采用成员管理协议方式实现成员管理与Mapreduce降低操作的同步化；其次，实行典型个体组降低操作代替全局个体降低操作，并定义二阶段缓冲算法；最后，通过第一阶段的缓冲进一步降低第二阶段Mapreduce操作的数据量，尽可能降低大数据带来的对算法负面影响。在此基础上，利用人造大数据测试集和KDD CUP 99入侵测试集进行仿真，实验结果表明，该算法既能保证聚类精度要求又可有效加快算法运行效率。
A New Approach to Lung Image Segmentation using Fuzzy Possibilistic C-Means Algorithm
Gomathi, M
2010-01-01
Image segmentation is a vital part of image processing. Segmentation has its application widespread in the field of medical images in order to diagnose curious diseases. The same medical images can be segmented manually. But the accuracy of image segmentation using the segmentation algorithms is more when compared with the manual segmentation. In the field of medical diagnosis an extensive diversity of imaging techniques is presently available, such as radiography, computed tomography (CT) and magnetic resonance imaging (MRI). Medical image segmentation is an essential step for most consequent image analysis tasks. Although the original FCM algorithm yields good results for segmenting noise free images, it fails to segment images corrupted by noise, outliers and other imaging artifact. This paper presents an image segmentation approach using Modified Fuzzy C-Means (FCM) algorithm and Fuzzy Possibilistic c-means algorithm (FPCM). This approach is a generalized version of standard Fuzzy CMeans Clustering (FCM) ...
An Investigation into Fuzzy Clustering and Classification.
1984-07-01
Introduction to Fuzzy Sets The theory of fuzzy sets was developed by Lofti Zadeh in 1965(4). The impetus behind the introduction of the fuzzy set was...Syntactic Pattern Recoonition: An Introduction, Reading, Massachussetts, Addison-Wesley, 1978 4. Zadeh , Lofti A., "Fuzzy Sets", Information and...where the models based on crisp set theory fall short of providing a useful description of things, people, or places. So, as Professor Zadeh proposed
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Siewert Hugelier
2016-12-01
Full Text Available In order to investigate hyperspectral images, many techniques such as multivariate image analysis (MIA or multivariate curve resolution–alternating least squares (MCR–ALS can be applied. When focusing on the use of MCR–ALS, constraints are of the utmost importance for a correct resolution of the data into its individual contributions. In this article, a fuzzy clustering pattern recognition method (fuzzy C-means is applied on experimental data in order to improve the results obtained within the MCR–ALS analysis. The big advantage of a fuzzy clustering technique over a hard clustering technique, such as k-means, is that the algorithm determines the probability of a pixel to be assigned to a component, indicating that a pixel can be part of multiple clusters (or components. This is, of course, an important property for dealing with data in which a lot of overlap between the components in the spatial direction occurs. This article deals briefly with the implementation of the constraint into the MCR–ALS algorithm and then shows the application of the constraint on an oil-in-water emulsion obtained by Raman spectroscopy, in which the different components can be decomposed in a clearer way and the interface between the oil and water bubbles becomes more visible.
System control fuzzy neural sewage pumping stations using genetic algorithms
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Владлен Николаевич Кузнецов
2015-06-01
Full Text Available It is considered the system of management of sewage pumping station with regulators based on a neuron network with fuzzy logic. Linguistic rules for the controller based on fuzzy logic, maintaining the level of effluent in the receiving tank within the prescribed limits are developed. The use of genetic algorithms for neuron network training is shown.
Fuzzy Context- Free Languages. Part 2: Recognition and Parsing Algorithms
Asveld, Peter R.J.
2000-01-01
In a companion paper [9] we used fuzzy context-free grammars in order to model grammatical errors resulting in erroneous inputs for robust recognizing and parsing algorithms for fuzzy context-free languages. In particular, this approach enables us to distinguish between small errors ("tiny mistakes"
Ding, Li; Zhou, Runjing; Liu, Guiying
2010-08-01
Pulse wave of human body contains large amount of physiological and pathological information, so the degree of arteriosclerosis classification algorithm is study based on fuzzy pattern recognition in this paper. Taking the human's pulse wave as the research object, we can extract the characteristic of time and frequency domain of pulse signal, and select the parameters with a better clustering effect for arteriosclerosis identification. Moreover, the validity of characteristic parameters is verified by fuzzy ISODATA clustering method (FISOCM). Finally, fuzzy pattern recognition system can quantitatively distinguish the degree of arteriosclerosis with patients. By testing the 50 samples in the built pulse database, the experimental result shows that the algorithm is practical and achieves a good classification recognition result.
Fuzzy Dynamic Discrimination Algorithms for Distributed Knowledge Management Systems
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Vasile MAZILESCU
2010-12-01
Full Text Available A reduction of the algorithmic complexity of the fuzzy inference engine has the following property: the inputs (the fuzzy rules and the fuzzy facts can be divided in two parts, one being relatively constant for a long a time (the fuzzy rule or the knowledge model when it is compared to the second part (the fuzzy facts for every inference cycle. The occurrence of certain transformations over the constant part makes sense, in order to decrease the solution procurement time, in the case that the second part varies, but it is known at certain moments in time. The transformations attained in advance are called pre-processing or knowledge compilation. The use of variables in a Business Rule Management System knowledge representation allows factorising knowledge, like in classical knowledge based systems. The language of the first-degree predicates facilitates the formulation of complex knowledge in a rigorous way, imposing appropriate reasoning techniques. It is, thus, necessary to define the description method of fuzzy knowledge, to justify the knowledge exploiting efficiency when the compiling technique is used, to present the inference engine and highlight the functional features of the pattern matching and the state space processes. This paper presents the main results of our project PR356 for designing a compiler for fuzzy knowledge, like Rete compiler, that comprises two main components: a static fuzzy discrimination structure (Fuzzy Unification Tree and the Fuzzy Variables Linking Network. There are also presented the features of the elementary pattern matching process that is based on the compiled structure of fuzzy knowledge. We developed fuzzy discrimination algorithms for Distributed Knowledge Management Systems (DKMSs. The implementations have been elaborated in a prototype system FRCOM (Fuzzy Rule COMpiler.
Chen, Liang; Tokuda, N
2002-01-01
By exploiting the Fourier series expansion, we have developed a new constructive method of automatically generating a multivariable fuzzy inference system from any given sample set with the resulting multivariable function being constructed within any specified precision to the original sample set. The given sample sets are first decomposed into a cluster of simpler sample sets such that a single input fuzzy system is constructed readily for a sample set extracted directly from the cluster independent of the other variables. Once the relevant fuzzy rules and membership functions are constructed for each of the variables completely independent of the other variables, the resulting decomposed fuzzy rules and membership functions are integrated back into the fuzzy system appropriate for the original sample set requiring only a moderate cost of computation in the required decomposition and composition processes. After proving two basic theorems which we need to ensure the validity of the decomposition and composition processes of the system construction, we have demonstrated a constructive algorithm of a multivariable fuzzy system. Exploiting an implicit error bound analysis available at each of the construction steps, the present Fourier method is capable of implementing a more stable fuzzy system than the power series expansion method of ParNeuFuz and PolyNeuFuz, covering and implementing a wider range of more robust applications.
Data clustering algorithms and applications
Aggarwal, Charu C
2013-01-01
Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains.The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as fea
Improved fuzzy identification method based on Hough transformation and fuzzy clustering
Institute of Scientific and Technical Information of China (English)
刘福才; 路平立; 潘江华; 裴润
2004-01-01
This paper presents an approach that is useful for the identification of a fuzzy model in SISO system. The initial values of cluster centers are identified by the Hough transformation, which considers the linearity and continuity of given input-output data, respectively. For the premise parts parameters identification, we use fuzzy-C-means clustering method. The consequent parameters are identified based on recursive least square. This method not only makes approximation more accurate, but also let computation be simpler and the procedure is realized more easily. Finally, it is shown that this method is useful for the identification of a fuzzy model by simulation.
Malleable Fuzzy Local Median C Means Algorithm for Effective Biomedical Image Segmentation
Rajendran, Arunkumar; Balakrishnan, Nagaraj; Varatharaj, Mithya
2016-12-01
The traditional way of clustering plays an effective role in the field of segmentation which was developed to be more effective and also in the recent development the extraction of contextual information can be processed with ease. This paper presents a modified Fuzzy C-Means (FCM) algorithm that provides the better segmentation in the contour grayscale regions of the biomedical images where effective cluster is needed. Malleable Fuzzy Local Median C-Means (M-FLMCM) is the proposed algorithm, proposed to overcome the disadvantage of the traditional FCM method in which the convergence time requirement is more, lack of ability to remove the noise, and the inability to cluster the contour region such as images. M-FLMCM shows promising results in the experiment with real-world biomedical images. The experiment results, with 96 % accuracy compared to the other algorithms.
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C.R.Bharathi
2012-11-01
Full Text Available Speech is the most vital skill of communication. Stammering is speech which is hesitant, stumbling, tense or jerky to the extent that it causes anxiety to the speaker. In the existing system, there are many effective treatments for the problem of stammering. Speech-language therapy is the treatment for most kids with speech and/or language disorders. In this work, mild level of mental retardation (MR children speech samples were taken for consideration. The proposed work is, the acute spot must be identified for affording speech training to the speech disordered children. To begin with the proposed work, initially Clustering of speech is done using Fuzzy C-means Clustering Algorithm. Feature Extraction is implemented using Mel Frequency Cepstrum Coefficients (MFCC and dimensionality reduction of features extracted is implemented using Principal Component Analysis (PCA. Finally the features were clustered using Fuzzy C-Means algorithm and compared with SVM classifier output[13].
Fuzzy Genetic Algorithm Based on Principal Operation and Inequity Degree
Li, Fachao; Jin, Chenxia
In this paper, starting from the structure of fuzzy information, by distinguishing principal indexes and assistant indexes, give comparison of fuzzy information on synthesizing effect and operation of fuzzy optimization on principal indexes transformation, further, propose axiom system of fuzzy inequity degree from essence of constraint, and give an instructive metric method; Then, combining genetic algorithm, give fuzzy optimization methods based on principal operation and inequity degree (denoted by BPO&ID-FGA, for short); Finally, consider its convergence using Markov chain theory and analyze its performance through an example. All these indicate, BPO&ID-FGA can not only effectively merge decision consciousness into the optimization process, but possess better global convergence, so it can be applied to many fuzzy optimization problems.
Fuzzy Sets-based Control Rules for Terminating Algorithms
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Jose L. VERDEGAY
2002-01-01
Full Text Available In this paper some problems arising in the interface between two different areas, Decision Support Systems and Fuzzy Sets and Systems, are considered. The Model-Base Management System of a Decision Support System which involves some fuzziness is considered, and in that context the questions on the management of the fuzziness in some optimisation models, and then of using fuzzy rules for terminating conventional algorithms are presented, discussed and analyzed. Finally, for the concrete case of the Travelling Salesman Problem, and as an illustration of determination, management and using the fuzzy rules, a new algorithm easy to implement in the Model-Base Management System of any oriented Decision Support System is shown.
A New Neuro-Fuzzy Adaptive Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
ZHU Lili; ZHANG Huanchun; JING Yazhi
2003-01-01
Novel neuro-fuzzy techniques are used to dynamically control parameter settings of genetic algorithms (GAs). The benchmark routine is an adaptive genetic algorithm (AGA) that uses a fuzzy knowledge-based system to control GA parameters. The self-learning ability of the cerebellar model ariculation controller(CMAC) neural network makes it possible for on-line learning the knowledge on GAs throughout the run. Automatically designing and tuning the fuzzy knowledge-base system, neurofuzzy techniques based on CMAC can find the optimized fuzzy system for AGA by the renhanced learning method. The Results from initial experiments show a Dynamic Parametric AGA system designed by the proposed automatic method and indicate the general applicability of the neuro-fuzzy AGA to a wide range of combinatorial optimization.
Assembly and Disassembly Planning by using Fuzzy Logic & Genetic Algorithms
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L. M. Galantucci
2004-06-01
Full Text Available The authors propose the implementation of hybrid Fuzzy Logic-Genetic Algorithm (FL-GA methodology to plan the automatic assembly and disassembly sequence of products. The GA-Fuzzy Logic approach is implemented onto two levels. The first level of hybridization consists of the development of a Fuzzy controller for the parameters of an assembly or disassembly planner based on GAs. This controller acts on mutation probability and crossover rate in order to adapt their values dynamically while the algorithm runs. The second level consists of the identification of the optimal assembly or disassembly sequence by a Fuzzy function, in order to obtain a closer control of the technological knowledge of the assembly/disassembly process. Two case studies were analyzed in order to test the efficiency of the Fuzzy-GA methodologies.
Assembly and Disassembly Planning by using Fuzzy Logic & Genetic Algorithms
Directory of Open Access Journals (Sweden)
L.M. Galantucci
2008-11-01
Full Text Available The authors propose the implementation of hybrid Fuzzy Logic-Genetic Algorithm (FL-GA methodology to plan the automatic assembly and disassembly sequence of products. The GA-Fuzzy Logic approach is implemented onto two levels. The first level of hybridization consists of the development of a Fuzzy controller for the parameters of an assembly or disassembly planner based on GAs. This controller acts on mutation probability and crossover rate in order to adapt their values dynamically while the algorithm runs. The second level consists of the identification of the optimal assembly or disassembly sequence by a Fuzzy function, in order to obtain a closer control of the technological knowledge of the assembly/disassembly process. Two case studies were analyzed in order to test the efficiency of the Fuzzy-GA methodologies.
Regional SAR Image Segmentation Based on Fuzzy Clustering with Gamma Mixture Model
Li, X. L.; Zhao, Q. H.; Li, Y.
2017-09-01
Most of stochastic based fuzzy clustering algorithms are pixel-based, which can not effectively overcome the inherent speckle noise in SAR images. In order to deal with the problem, a regional SAR image segmentation algorithm based on fuzzy clustering with Gamma mixture model is proposed in this paper. First, initialize some generating points randomly on the image, the image domain is divided into many sub-regions using Voronoi tessellation technique. Each sub-region is regarded as a homogeneous area in which the pixels share the same cluster label. Then, assume the probability of the pixel to be a Gamma mixture model with the parameters respecting to the cluster which the pixel belongs to. The negative logarithm of the probability represents the dissimilarity measure between the pixel and the cluster. The regional dissimilarity measure of one sub-region is defined as the sum of the measures of pixels in the region. Furthermore, the Markov Random Field (MRF) model is extended from pixels level to Voronoi sub-regions, and then the regional objective function is established under the framework of fuzzy clustering. The optimal segmentation results can be obtained by the solution of model parameters and generating points. Finally, the effectiveness of the proposed algorithm can be proved by the qualitative and quantitative analysis from the segmentation results of the simulated and real SAR images.
Two-Way Regularized Fuzzy Clustering of Multiple Correspondence Analysis.
Kim, Sunmee; Choi, Ji Yeh; Hwang, Heungsun
2017-01-01
Multiple correspondence analysis (MCA) is a useful tool for investigating the interrelationships among dummy-coded categorical variables. MCA has been combined with clustering methods to examine whether there exist heterogeneous subclusters of a population, which exhibit cluster-level heterogeneity. These combined approaches aim to classify either observations only (one-way clustering of MCA) or both observations and variable categories (two-way clustering of MCA). The latter approach is favored because its solutions are easier to interpret by providing explicitly which subgroup of observations is associated with which subset of variable categories. Nonetheless, the two-way approach has been built on hard classification that assumes observations and/or variable categories to belong to only one cluster. To relax this assumption, we propose two-way fuzzy clustering of MCA. Specifically, we combine MCA with fuzzy k-means simultaneously to classify a subgroup of observations and a subset of variable categories into a common cluster, while allowing both observations and variable categories to belong partially to multiple clusters. Importantly, we adopt regularized fuzzy k-means, thereby enabling us to decide the degree of fuzziness in cluster memberships automatically. We evaluate the performance of the proposed approach through the analysis of simulated and real data, in comparison with existing two-way clustering approaches.
An Improved Fuzzy C-Means Algorithm for the Implementation of Demand Side Management Measures
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Ioannis Panapakidis
2017-09-01
Full Text Available Load profiling refers to a procedure that leads to the formulation of daily load curves and consumer classes regarding the similarity of the curve shapes. This procedure incorporates a set of unsupervised machine learning algorithms. While many crisp clustering algorithms have been proposed for grouping load curves into clusters, only one soft clustering algorithm is utilized for the aforementioned purpose, namely the Fuzzy C-Means (FCM algorithm. Since the benefits of soft clustering are demonstrated in a variety of applications, the potential of introducing a novel modification of the FCM in the electricity consumer clustering process is examined. Additionally, this paper proposes a novel Demand Side Management (DSM strategy for load management of consumers that are eligible for the implementation of Real-Time Pricing (RTP schemes. The DSM strategy is formulated as a constrained optimization problem that can be easily solved and therefore, making it a useful tool for retailers’ decision-making framework in competitive electricity markets.
Institute of Scientific and Technical Information of China (English)
毛海军; 王勇; 杭文; 于航; 何杰
2012-01-01
To optimize multi-distribution center location operation in two-level facilities logistics network, the main influential factors on locating distribution centers are extracted, and a comprehensive evaluation index system is set up. Firstly, the linguistic variables are represented by triangular fuzzy number to implement a comprehensive evaluation for candidate distribution centers. Secondly, the interval number priority degree function method is adopted to integrate the criteria index into the first criteria index, and the integrated project evaluation index value is used as the input of fuzzy clustering algorithm for clustering operation. The clustering validity index is designed to analyze the rationality of clustering results. Finally, the technique for order preference by similarity to ideal solution (TOPSIS) method is used to rank the candidate distribution centers within the clustering unit, and the locations and quantities of distribution centers are determined. The results of an application example show that when the membership function value is 0.740 2, the clustering validity index gets the smallest value 2.43. The operation can divide the candidate distribution centers into four clusters and select the distribution center location in each cluster, making the location results reasonable and more advantageous than other methods. Therefore, the proposed method is more effective in addressing multi-distribution center location problem.%为了优化二级设施物流网络中多配送中心的选址操作,提取了影响配送中心选址的主要因素,建立了一种综合评价指标体系.首先,将语言变量值用三角模糊数表示,对备选配送中心进行综合评价；然后,采用区间数优度函数法将二级准则指标集成到一级准则指标上,以集成后的方案评价指标值作为模糊聚类算法的输入进行聚类操作,并设计了聚类有效性指标以用于判断聚类结果合理性；最后,应用TOPSIS方法对各
Cluster Synchronization Algorithms
Xia, Weiguo; Cao, Ming
2010-01-01
This paper presents two approaches to achieving cluster synchronization in dynamical multi-agent systems. In contrast to the widely studied synchronization behavior, where all the coupled agents converge to the same value asymptotically, in the cluster synchronization problem studied in this paper,
A Novel Evolutionary-Fuzzy Control Algorithm for Complex Systems
Institute of Scientific and Technical Information of China (English)
王攀; 徐承志; 冯珊; 徐爱华
2002-01-01
This paper presents an adaptive fuzzy control scheme based on modified genetic algorithm. In the control scheme, genetic algorithm is used to optimze the nonlinear quantization functions of the controller and some key parameters of the adaptive control algorithm. Simulation results show that this control scheme has satisfactory performance in MIMO systems, chaotic systems and delay systems.
Comparative Analysis of K-Means and Fuzzy C-Means Algorithms
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Soumi Ghosh
2013-05-01
Full Text Available In the arena of software, data mining technology has been considered as useful means for identifying patterns and trends of large volume of data. This approach is basically used to extract the unknown pattern from the large set of data for business as well as real time applications. It is a computational intelligence discipline which has emerged as a valuable tool for data analysis, new knowledge discovery and autonomous decision making. The raw, unlabeled data from the large volume of dataset can be classified initially in an unsupervised fashion by using cluster analysis i.e. clustering the assignment of a set of observations into clusters so that observations in the same cluster may be in some sense be treated as similar. The outcome of the clustering process and efficiency of its domain application are generally determined through algorithms. There are various algorithms which are used to solve this problem. In this research work two important clustering algorithms namely centroid based K-Means and representative object based FCM (Fuzzy C-Means clustering algorithms are compared. These algorithms are applied and performance is evaluated on the basis of the efficiency of clustering output. The numbers of data points as well as the number of clusters are the factors upon which the behaviour patterns of both the algorithms are analyzed. FCM produces close results to K-Means clustering but it still requires more computation time than K-Means clustering.
Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering.
Gong, Maoguo; Zhou, Zhiqiang; Ma, Jingjing
2012-04-01
This paper presents an unsupervised distribution-free change detection approach for synthetic aperture radar (SAR) images based on an image fusion strategy and a novel fuzzy clustering algorithm. The image fusion technique is introduced to generate a difference image by using complementary information from a mean-ratio image and a log-ratio image. In order to restrain the background information and enhance the information of changed regions in the fused difference image, wavelet fusion rules based on an average operator and minimum local area energy are chosen to fuse the wavelet coefficients for a low-frequency band and a high-frequency band, respectively. A reformulated fuzzy local-information C-means clustering algorithm is proposed for classifying changed and unchanged regions in the fused difference image. It incorporates the information about spatial context in a novel fuzzy way for the purpose of enhancing the changed information and of reducing the effect of speckle noise. Experiments on real SAR images show that the image fusion strategy integrates the advantages of the log-ratio operator and the mean-ratio operator and gains a better performance. The change detection results obtained by the improved fuzzy clustering algorithm exhibited lower error than its preexistences.
Logistics distribution centers location problem and algorithm under fuzzy environment
Yang, Lixing; Ji, Xiaoyu; Gao, Ziyou; Li, Keping
2007-11-01
Distribution centers location problem is concerned with how to select distribution centers from the potential set so that the total relevant cost is minimized. This paper mainly investigates this problem under fuzzy environment. Consequentially, chance-constrained programming model for the problem is designed and some properties of the model are investigated. Tabu search algorithm, genetic algorithm and fuzzy simulation algorithm are integrated to seek the approximate best solution of the model. A numerical example is also given to show the application of the algorithm.
Fuzzy C-Means Clustering Based Phonetic Tied-Mixture HMM in Speech Recognition
Institute of Scientific and Technical Information of China (English)
XU Xiang-hua; ZHU Jie; GUO Qiang
2005-01-01
A fuzzy clustering analysis based phonetic tied-mixture HMM(FPTM) was presented to decrease parameter size and improve robustness of parameter training. FPTM was synthesized from state-tied HMMs by a modified fuzzy C-means clustering algorithm. Each Gaussian codebook of FPTM was built from Gaussian components within the same root node in phonetic decision tree. The experimental results on large vocabulary Mandarin speech recognition show that compared with conventional phonetic tied-mixture HMM and state-tied HMM with approximately the same number of Gaussian mixtures, FPTM achieves word error rate reductions by 4.84% and 13.02 % respectively. Combining the two schemes of mixing weights pruning and Gaussian centers fuzzy merging, a significantly parameter size reduction was achieved with little impact on recognition accuracy.
Institute of Scientific and Technical Information of China (English)
牛艳庆
2015-01-01
Research in this paper aims to find some novel approaches to detecting the community structures in complex networks.To reduce the subjectivity in the existing algorithms , we detect community structure in complex networks based on quantum fuzzy clustering analysis method .The experimental results show that this method can give satisfactory results .%研究了复杂网络的社团结构特性，探讨了复杂网络的社团结构探测算法。针对现有算法中判断社团结构时的主观性问题，提出了量子模糊聚类算法，并将该算法用于复杂网络社团结构的探测。实验结果表明：该算法可以准确、有效地探测到网络中实际存在的社团结构。
Identification of certain cancer-mediating genes using Gaussian fuzzy cluster validity index
Indian Academy of Sciences (India)
Anupam Ghosh; Rajat K De
2015-10-01
In this article, we have used an index, called Gaussian fuzzy index (GFI), recently developed by the authors, based on the notion of fuzzy set theory, for validating the clusters obtained by a clustering algorithm applied on cancer gene expression data. GFI is then used for the identification of genes that have altered quite significantly from normal state to carcinogenic state with respect to their mRNA expression patterns. The effectiveness of the methodology has been demonstrated on three gene expression cancer datasets dealing with human lung, colon and leukemia. The performance of GFI is compared with 19 exiting cluster validity indices. The results are appropriately validated biologically and statistically. In this context, we have used biochemical pathways, -value statistics of GO attributes, -test and -score for the validation of the results. It has been reported that GFI is capable of identifying high-quality enriched clusters of genes, and thereby is able to select more cancer-mediating genes.
Yu, Zhenhua; Fu, Xiao; Cai, Yuanli; Vuran, Mehmet C.
2011-01-01
A reliable energy-efficient multi-level routing algorithm in wireless sensor networks is proposed. The proposed algorithm considers the residual energy, number of the neighbors and centrality of each node for cluster formation, which is critical for well-balanced energy dissipation of the network. In the algorithm, a knowledge-based inference approach using fuzzy Petri nets is employed to select cluster heads, and then the fuzzy reasoning mechanism is used to compute the degree of reliability in the route sprouting tree from cluster heads to the base station. Finally, the most reliable route among the cluster heads can be constructed. The algorithm not only balances the energy load of each node but also provides global reliability for the whole network. Simulation results demonstrate that the proposed algorithm effectively prolongs the network lifetime and reduces the energy consumption. PMID:22163802
Improved Gravitational Search Algorithm (GSA Using Fuzzy Logic
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Omid Mokhlesi
2013-04-01
Full Text Available Researchers tendency to use different collective intelligence as the search methods to optimize complex engineering problems has increased because of the high performance of this algorithms. Gravitational search algorithm (GSA is among these algorithms. This algorithm is inspired by Newton's laws of physics and gravitational attraction. Random masses are agents who have searched for the space. This paper presents a new Fuzzy Population GSA model called FPGSA. The proposed method is a combination of parametric fuzzy controller and gravitational search algorithm. The space being searched using this combined reasonable and accurate method. In the collective intelligence algorithms, population size influences the final answer so that for a large population, a better response is obtained but the algorithm execution time is longer. To overcome this problem, a new parameter called the dispersion coefficient is added to the algorithm. Implementation results show that by controlling this factor, system performance can be improved.
Institute of Scientific and Technical Information of China (English)
吕一; 杨明
2012-01-01
遗传算法（GA）被广泛地应用在聚类算法中.但是当数据点多时,其计算量大的问题是不容忽视的.针对与遗传算法相结合的FCSS算法中的这一不足,主要通过研究了GA初始化种群的选取方法,对GA-FCSS算法进行了改进.实验数据表明：改进的GA-FCSS算法在收敛速度方面有令人满意的效果.%The genetic algorithm (GA) is wildly applied into the clustering algorithm, but when the data points is excessive, the algorithm calculation amount takes too long . According to the shortage of the algorithm integrated with the FCSS algorithm and the genetic algorithm, the main research is propose a new method to improved the select initial population in GA - FCSS. The experiment show result that the new method of select initial population has better effects in the speed of converges.
Effective FCM noise clustering algorithms in medical images.
Kannan, S R; Devi, R; Ramathilagam, S; Takezawa, K
2013-02-01
The main motivation of this paper is to introduce a class of robust non-Euclidean distance measures for the original data space to derive new objective function and thus clustering the non-Euclidean structures in data to enhance the robustness of the original clustering algorithms to reduce noise and outliers. The new objective functions of proposed algorithms are realized by incorporating the noise clustering concept into the entropy based fuzzy C-means algorithm with suitable noise distance which is employed to take the information about noisy data in the clustering process. This paper presents initial cluster prototypes using prototype initialization method, so that this work tries to obtain the final result with less number of iterations. To evaluate the performance of the proposed methods in reducing the noise level, experimental work has been carried out with a synthetic image which is corrupted by Gaussian noise. The superiority of the proposed methods has been examined through the experimental study on medical images. The experimental results show that the proposed algorithms perform significantly better than the standard existing algorithms. The accurate classification percentage of the proposed fuzzy C-means segmentation method is obtained using silhouette validity index.
An Algorithm for Mining Multidimensional Fuzzy Association Rules
Khare, Neelu; Pardasani, K R
2009-01-01
Multidimensional association rule mining searches for interesting relationship among the values from different dimensions or attributes in a relational database. In this method the correlation is among set of dimensions i.e., the items forming a rule come from different dimensions. Therefore each dimension should be partitioned at the fuzzy set level. This paper proposes a new algorithm for generating multidimensional association rules by utilizing fuzzy sets. A database consisting of fuzzy transactions, the Apriory property is employed to prune the useless candidates, itemsets.
Manipulator Neural Network Control Based on Fuzzy Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
The three-layer forward neural networks are used to establish the inverse kinem a tics models of robot manipulators. The fuzzy genetic algorithm based on the line ar scaling of the fitness value is presented to update the weights of neural net works. To increase the search speed of the algorithm, the crossover probability and the mutation probability are adjusted through fuzzy control and the fitness is modified by the linear scaling method in FGA. Simulations show that the propo sed method improves considerably the precision of the inverse kinematics solutio ns for robot manipulators and guarantees a rapid global convergence and overcome s the drawbacks of SGA and the BP algorithm.
Fuzzy Flexible Resource Constrained Project Scheduling Based on Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
查鸿; 张连营
2014-01-01
Both fuzzy temporal constraint and flexible resource constraint are considered in project scheduling. In order to obtain an optimal schedule, we propose a genetic algorithm integrated with concepts on fuzzy set theory as well as specialized coding and decoding mechanism. An example demonstrates that the proposed approach can assist the project managers to obtain the optimal schedule effectively and make the correct decision on skill training before a project begins.
Classification of protein profiles using fuzzy clustering techniques
DEFF Research Database (Denmark)
Karemore, Gopal; Mullick, Jhinuk B.; Sujatha, R.
2010-01-01
-to-day variation, artifacts due to experimental strategies, inherent uncertainty in pumping procedure which are very common activities during HPLC-LIF experiment. Under these circumstances we demonstrate how fuzzy clustering algorithm like Gath Geva followed by sammon mapping outperform...... PCA mapping in classifying various cancers from healthy spectra with classification rate up to 95 % from 60%. Methods are validated using various clustering indexes and shows promising improvement in developing optical pathology like HPLC-LIF for early detection of various...
Institute of Scientific and Technical Information of China (English)
孟安波; 卢海明; 郭壮志
2016-01-01
Optimized the FCM clustering by the proposed CSO ( CSO-FCM) is introduced to diagnose the fault of transformer in order to conquer the shortages of FCM clustering.The combination of dissolved gas analysis and FCM clustering is effective on improving the accuracy rate of power transformer fault diagnosis, but the result of FCM cluste-ring is unstable and easy getting stuck in a local optimum.The CSO algorithm includes horizon cross as well as verti-cal cross, whose combining can enhance the global convergent ability while the introduction of competitive mechanism drives the potential solutions approximate the global optima in an accelerating fashion without sacrificing the conver-gence speed.This novel method effectively compensates the demerits of single intelligent algorithm, which not only has the ability to dispose the unstable information of fuzzy theory, also has an advantage of global convergence of CSO. Simulation and case analysis indicate that, compared with the traditional FCM clustering, the CSO-FCM clustering can obtain high performance clustering center and effectively raise the accuracy and diagnosis speed of power transformer fault diagnosis.%针对FCM（模糊C－均值聚类）在变压器故障诊断中的不足，提出采用纵横交叉算法优化FCM （ CSO－FCM）聚类来进行故障诊断。溶解气体分析与FCM相结合，能有效提高变压器故障诊断的准确率，但FCM存在聚类结果不稳定和容易陷入局部最优等问题。而纵横交叉算法是一种基于种群的随机搜索算法，在算法中首次提出了维局部最优概念和纵横交叉双搜索思想。实验证明，相比其它主流群智能优化算法，CSO算法在解决维数灾问题和收敛精度问题方面取得了较大突破，能有效克服局部最优的问题。新诊断模型有效弥补了单一诊断法的不足，拥有全局收敛性强和处理模糊信息的能力。实例分析表明，该方法与传统FCM相比，能获得
Discovering Fuzzy Censored Classification Rules (Fccrs: A Genetic Algorithm Approach
Directory of Open Access Journals (Sweden)
Renu Bala
2012-08-01
Full Text Available Classification Rules (CRs are often discovered in the form of ‘If-Then’ Production Rules (PRs. PRs, beinghigh level symbolic rules, are comprehensible and easy to implement. However, they are not capable ofdealing with cognitive uncertainties like vagueness and ambiguity imperative to real word decision makingsituations. Fuzzy Classification Rules (FCRs based on fuzzy logic provide a framework for a flexiblehuman like reasoning involving linguistic variables. Moreover, a classification system consisting of simple‘If-Then’ rules is not competent in handling exceptional circumstances. In this paper, we propose aGenetic Algorithm approach to discover Fuzzy Censored Classification Rules (FCCRs. A FCCR is aFuzzy Classification Rule (FCRs augmented with censors. Here, censors are exceptional conditions inwhich the behaviour of a rule gets modified. The proposed algorithm works in two phases. In the firstphase, the Genetic Algorithm discovers Fuzzy Classification Rules. Subsequently, these FuzzyClassification Rules are mutated to produce FCCRs in the second phase. The appropriate encodingscheme, fitness function and genetic operators are designed for the discovery of FCCRs. The proposedapproach for discovering FCCRs is then illustrated on a synthetic dataset.
Kernel Clustering with a Differential Harmony Search Algorithm for Scheme Classification
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Yu Feng
2017-01-01
Full Text Available This paper presents a kernel fuzzy clustering with a novel differential harmony search algorithm to coordinate with the diversion scheduling scheme classification. First, we employed a self-adaptive solution generation strategy and differential evolution-based population update strategy to improve the classical harmony search. Second, we applied the differential harmony search algorithm to the kernel fuzzy clustering to help the clustering method obtain better solutions. Finally, the combination of the kernel fuzzy clustering and the differential harmony search is applied for water diversion scheduling in East Lake. A comparison of the proposed method with other methods has been carried out. The results show that the kernel clustering with the differential harmony search algorithm has good performance to cooperate with the water diversion scheduling problems.
Prediction of Adsorption of Cadmium by Hematite Using Fuzzy C-Means Clustering Technique
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Sriparna Das
2012-11-01
Full Text Available Clustering is partitioning of data set into subsets (clusters, so that the data in each subset share some common trait. In this paper, an algorithm has been proposed based on Fuzzy C-means clustering technique for prediction of adsorption of cadmium by hematite. The original data elements have been used for clustering the random data set. The random data have been generated within the minimum and maximum value of test data. The proposed algorithm has been applied on random dataset considering the original data set as initial cluster center. A threshold value has been taken to make the boundary around the clustering center. Finally, after execution of algorithm, modified cluster centers have been computed based on each initial cluster center. The modified cluster centers have been treated as predicted data set. The algorithm has been tested in prediction of adsorption of cadmium by hematite. The error has been calculated between the original data and predicted data. It has been observed that the proposed algorithm has given better result than the previous applied methods.
FCM Clustering Algorithms for Segmentation of Brain MR Images
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Yogita K. Dubey
2016-01-01
Full Text Available The study of brain disorders requires accurate tissue segmentation of magnetic resonance (MR brain images which is very important for detecting tumors, edema, and necrotic tissues. Segmentation of brain images, especially into three main tissue types: Cerebrospinal Fluid (CSF, Gray Matter (GM, and White Matter (WM, has important role in computer aided neurosurgery and diagnosis. Brain images mostly contain noise, intensity inhomogeneity, and weak boundaries. Therefore, accurate segmentation of brain images is still a challenging area of research. This paper presents a review of fuzzy c-means (FCM clustering algorithms for the segmentation of brain MR images. The review covers the detailed analysis of FCM based algorithms with intensity inhomogeneity correction and noise robustness. Different methods for the modification of standard fuzzy objective function with updating of membership and cluster centroid are also discussed.
FUZZY CLUSTERING BASED BAYESIAN FRAMEWORK TO PREDICT MENTAL HEALTH PROBLEMS AMONG CHILDREN
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M R Sumathi
2017-04-01
Full Text Available According to World Health Organization, 10-20% of children and adolescents all over the world are experiencing mental disorders. Correct diagnosis of mental disorders at an early stage improves the quality of life of children and avoids complicated problems. Various expert systems using artificial intelligence techniques have been developed for diagnosing mental disorders like Schizophrenia, Depression, Dementia, etc. This study focuses on predicting basic mental health problems of children, like Attention problem, Anxiety problem, Developmental delay, Attention Deficit Hyperactivity Disorder (ADHD, Pervasive Developmental Disorder(PDD, etc. using the machine learning techniques, Bayesian Networks and Fuzzy clustering. The focus of the article is on learning the Bayesian network structure using a novel Fuzzy Clustering Based Bayesian network structure learning framework. The performance of the proposed framework was compared with the other existing algorithms and the experimental results have shown that the proposed framework performs better than the earlier algorithms.
Tuning of a neuro-fuzzy controller by genetic algorithm.
Seng, T L; Bin Khalid, M; Yusof, R
1999-01-01
Due to their powerful optimization property, genetic algorithms (GAs) are currently being investigated for the development of adaptive or self-tuning fuzzy logic control systems. This paper presents a neuro-fuzzy logic controller (NFLC) where all of its parameters can be tuned simultaneously by GA. The structure of the controller is based on the radial basis function neural network (RBF) with Gaussian membership functions. The NFLC tuned by GA can somewhat eliminate laborious design steps such as manual tuning of the membership functions and selection of the fuzzy rules. The GA implementation incorporates dynamic crossover and mutation probabilistic rates for faster convergence. A flexible position coding strategy of the NFLC parameters is also implemented to obtain near optimal solutions. The performance of the proposed controller is compared with a conventional fuzzy controller and a PID controller tuned by GA. Simulation results show that the proposed controller offers encouraging advantages and has better performance.
一种基于模糊核聚类的脑部磁共振图像分割算法%An algorithm for MRI brain image segmentation based on fuzzy kernel clustering
Institute of Scientific and Technical Information of China (English)
相艳; 贺建峰; 易三莉; 徐家萍; 张娴文
2013-01-01
目的 针对普通模糊核聚类算法(kernel fuzzy c-means clustering algorithm,KFCM)存在的随机选择初始聚类中心的问题,本文提出一种根据直方图得到确定的初始聚类中心的模糊核聚类算法,以更快速地分割脑部磁共振图像.方法 首先利用区域生长法和形态学方法对原始脑部磁共振图像进行预处理,提取脑实质,然后计算出预处理图像的直方图,将直方图的4个峰值作为模糊核聚类的初始聚类中心,最后利用模糊核聚类算法对脑实质进行分割.结果 本文算法能有效地提取出脑组织中的白质(white matter,WM)、灰质(grey matter,GM)和脑脊髓液(cerebral spinal fluid,CSF).与普通模糊核聚类算法相比,该算法的目标函数能更快地达到平稳,从而缩短运行时间.结论 本文算法与随机选择聚类中心的模糊核聚类算法相比,可减少迭代次数,更快地得到分割结果.
FUZZY BASED CLUSTERING AND ENERGY EFFICIENT ROUTING FOR UNDERWATER WIRELESS SENSOR NETWORKS
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Sihem Souiki
2015-03-01
Full Text Available Underwater Wireless Sensor Network (UWSN is a particular kind of sensor networks which is characterized by using acoustic channels for communication. UWSN is challenged by great issues specially the energy supply of sensor node which can be wasted rapidly by several factors. The most proposed routing protocols for terrestrial sensor networks are not adequate for UWSN, thus new design of routing protocols must be adapted to this constrain. In this paper we propose two new clustering algorithms based on Fuzzy C-Means mechanisms. In the first proposition, the cluster head is elected initially based on the closeness to the center of the cluster, then the node having the higher residual energy elects itself as a cluster head. All non-cluster head nodes transmit sensed data to the cluster head. This latter performs data aggregation and transmits the data directly to the base station. The second algorithm uses the same principle in forming clusters and electing cluster heads but operates in multi-hop mode to forward data from cluster heads to the underwater sink (uw-sink. Furthermore the two proposed algorithms are tested for static and dynamic deployment. Simulation results demonstrate the effectiveness of the proposed algorithms resulting in an extension of the network lifetime.
Maximum-entropy clustering algorithm and its global convergence analysis
Institute of Scientific and Technical Information of China (English)
ZHANG; Zhihua
2001-01-01
［1］Bezdek, J. C., Pattern Recognition with Fuzzy Objective Function Algorithm. New York: Plenum, 1981.［2］Krishnapuram, R., Keller, J., A possibilistic approach to clustering, IEEE Trans. on Fuzzy Systems, 1993, 1(2): 98.［3］Yair, E., Zeger, K., Gersho, A., Competitive learning and soft competition for vector quantizer design, IEEE Trans on Signal Processing, 1992, 40(2): 294.［4］Pal, N. R., Bezdek, J. C., Tsao, E. C. K., Generalized clustering networks and Kohonen's self-organizing scheme, IEEE Trans on Neural Networks, 1993, 4(4): 549.［5］Karayiannis, N. B., Bezdek, J. C., Pal, N. R. et al., Repair to GLVQ: a new family of competitive learning schemes, IEEE Trans on Neural Networks, 1996, 7(5): 1062.［6］Karayiannis, N. B., Pai, P. I., Fuzzy algorithms for learning vector quantization, IEEE Trans. on Neural Networks, 1996, 7(5): 1196.［7］Karayiannis, N. B., A methodology for constructing fuzzy algorithms for learning vector quantization, IEEE Trans. on Neural Networks, 1997, 8(3): 505.［8］Karayiannis, N. B., Bezdek, J. C., An integrated approach to fuzzy learning vector quantization and fuzzy C-Means clustering, IEEE Trans. on Fuzzy Systems, 1997, 5(4): 622.［9］Li Xing-si, An efficient approach to nonlinear minimax problems, Chinese Science Bulletin? 1992, 37(10): 802.［10］Li Xing-si, An efficient approach to a class of non-smooth optimization problems, Science in China, Series A,1994, 37(3): 323.［11］. Zangwill, W., Non-linear Programming: A Unified Approach, Englewood Cliffs: Prentice-Hall, 1969.［12］. Fletcher, R., Practical Methods of Optimization,2nd ed., New York: John Wiley & Sons, 1987.［13］. Zhang Zhihua, Zheng Nanning, Wang Tianshu, Behavioral analysis and improving of generalized LVQ neural network, Acta Automatica Sinica, 1999, 25(5): 582.［14］. Kirkpatrick, S., Gelatt, C. D., Vecchi, M. P., Optimization by simulated annealing, Science, 1983, 220(3): 671.［15］. Ross, K., Deterministic annealing for
Directory of Open Access Journals (Sweden)
Sohel Rana
2015-03-01
Full Text Available The Wireless Sensor Network (WSN is made up with small batteries powered sensor devices with lim-ited energy resources within it. These sensor nodes are used to monitor physical or environmental conditions and to pass their data through the wireless network to the main location. One of the crucial issues in wireless sensor network is to create a more energy efficient system. Clustering is one kind of mechanism in Wireless Sensor Networks to prolong the network lifetime and to reduce network energy consumption. In this paper, we propose a new routing protocol called Fuzzy Based Energy Effi-cient Multiple Cluster Head Selection Routing Protocol (FEMCHRP for Wireless Sensor Network. The routing process involves the Clustering of nodes and the selection of Cluster Head (CH nodes of these clusters which sends all the information to the Cluster Head Leader (CHL. After that, the cluster head leaders send aggregated data to the Base Station (BS. The selection of cluster heads and cluster head leaders is performed by using fuzzy logic and the data transmission process is performed by shortest energy path which is selected applying Dijkstra Algorithm. The simulation results of this research are compared with other protocols BCDCP, CELRP and ECHERP to evaluate the performance of the proposed routing protocol. The evaluation concludes that the proposed routing protocol is better in prolonging network lifetime and balancing energy consumption.
A NEW UNSUPERVISED CLASSIFICATION ALGORITHM FOR POLARIMETRIC SAR IMAGES BASED ON FUZZY SET THEORY
Institute of Scientific and Technical Information of China (English)
Fu Yusheng; Xie Yan; Pi Yiming; Hou Yinming
2006-01-01
In this letter, a new method is proposed for unsupervised classification of terrain types and man-made objects using POLarimetric Synthetic Aperture Radar (POLSAR) data. This technique is a combination of the usage of polarimetric information of SAR images and the unsupervised classification method based on fuzzy set theory. Image quantization and image enhancement are used to preprocess the POLSAR data. Then the polarimetric information and Fuzzy C-Means (FCM) clustering algorithm are used to classify the preprocessed images. The advantages of this algorithm are the automated classification, its high classification accuracy, fast convergence and high stability. The effectiveness of this algorithm is demonstrated by experiments using SIR-C/X-SAR (Spaceborne Imaging Radar-C/X-band Synthetic Aperture Radar) data.
DCT-Yager FNN: a novel Yager-based fuzzy neural network with the discrete clustering technique.
Singh, A; Quek, C; Cho, S Y
2008-04-01
Earlier clustering techniques such as the modified learning vector quantization (MLVQ) and the fuzzy Kohonen partitioning (FKP) techniques have focused on the derivation of a certain set of parameters so as to define the fuzzy sets in terms of an algebraic function. The fuzzy membership functions thus generated are uniform, normal, and convex. Since any irregular training data is clustered into uniform fuzzy sets (Gaussian, triangular, or trapezoidal), the clustering may not be exact and some amount of information may be lost. In this paper, two clustering techniques using a Kohonen-like self-organizing neural network architecture, namely, the unsupervised discrete clustering technique (UDCT) and the supervised discrete clustering technique (SDCT), are proposed. The UDCT and SDCT algorithms reduce this data loss by introducing nonuniform, normal fuzzy sets that are not necessarily convex. The training data range is divided into discrete points at equal intervals, and the membership value corresponding to each discrete point is generated. Hence, the fuzzy sets obtained contain pairs of values, each pair corresponding to a discrete point and its membership grade. Thus, it can be argued that fuzzy membership functions generated using this kind of a discrete methodology provide a more accurate representation of the actual input data. This fact has been demonstrated by comparing the membership functions generated by the UDCT and SDCT algorithms against those generated by the MLVQ, FKP, and pseudofuzzy Kohonen partitioning (PFKP) algorithms. In addition to these clustering techniques, a novel pattern classifying network called the Yager fuzzy neural network (FNN) is proposed in this paper. This network corresponds completely to the Yager inference rule and exhibits remarkable generalization abilities. A modified version of the pseudo-outer product (POP)-Yager FNN called the modified Yager FNN is introduced that eliminates the drawbacks of the earlier network and yi- elds
Structuring heterogeneous biological information using fuzzy clustering of k-partite graphs
Directory of Open Access Journals (Sweden)
Theis Fabian J
2010-10-01
Full Text Available Abstract Background Extensive and automated data integration in bioinformatics facilitates the construction of large, complex biological networks. However, the challenge lies in the interpretation of these networks. While most research focuses on the unipartite or bipartite case, we address the more general but common situation of k-partite graphs. These graphs contain k different node types and links are only allowed between nodes of different types. In order to reveal their structural organization and describe the contained information in a more coarse-grained fashion, we ask how to detect clusters within each node type. Results Since entities in biological networks regularly have more than one function and hence participate in more than one cluster, we developed a k-partite graph partitioning algorithm that allows for overlapping (fuzzy clusters. It determines for each node a degree of membership to each cluster. Moreover, the algorithm estimates a weighted k-partite graph that connects the extracted clusters. Our method is fast and efficient, mimicking the multiplicative update rules commonly employed in algorithms for non-negative matrix factorization. It facilitates the decomposition of networks on a chosen scale and therefore allows for analysis and interpretation of structures on various resolution levels. Applying our algorithm to a tripartite disease-gene-protein complex network, we were able to structure this graph on a large scale into clusters that are functionally correlated and biologically meaningful. Locally, smaller clusters enabled reclassification or annotation of the clusters' elements. We exemplified this for the transcription factor MECP2. Conclusions In order to cope with the overwhelming amount of information available from biomedical literature, we need to tackle the challenge of finding structures in large networks with nodes of multiple types. To this end, we presented a novel fuzzy k-partite graph partitioning
Combinatorial Clustering Algorithm of Quantum-Behaved Particle Swarm Optimization and Cloud Model
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Mi-Yuan Shan
2013-01-01
Full Text Available We propose a combinatorial clustering algorithm of cloud model and quantum-behaved particle swarm optimization (COCQPSO to solve the stochastic problem. The algorithm employs a novel probability model as well as a permutation-based local search method. We are setting the parameters of COCQPSO based on the design of experiment. In the comprehensive computational study, we scrutinize the performance of COCQPSO on a set of widely used benchmark instances. By benchmarking combinatorial clustering algorithm with state-of-the-art algorithms, we can show that its performance compares very favorably. The fuzzy combinatorial optimization algorithm of cloud model and quantum-behaved particle swarm optimization (FCOCQPSO in vague sets (IVSs is more expressive than the other fuzzy sets. Finally, numerical examples show the clustering effectiveness of COCQPSO and FCOCQPSO clustering algorithms which are extremely remarkable.
Fuzzy clustering of physicochemical and biochemical properties of amino acids.
Saha, Indrajit; Maulik, Ujjwal; Bandyopadhyay, Sanghamitra; Plewczynski, Dariusz
2012-08-01
In this article, we categorize presently available experimental and theoretical knowledge of various physicochemical and biochemical features of amino acids, as collected in the AAindex database of known 544 amino acid (AA) indices. Previously reported 402 indices were categorized into six groups using hierarchical clustering technique and 142 were left unclustered. However, due to the increasing diversity of the database these indices are overlapping, therefore crisp clustering method may not provide optimal results. Moreover, in various large-scale bioinformatics analyses of whole proteomes, the proper selection of amino acid indices representing their biological significance is crucial for efficient and error-prone encoding of the short functional sequence motifs. In most cases, researchers perform exhaustive manual selection of the most informative indices. These two facts motivated us to analyse the widely used AA indices. The main goal of this article is twofold. First, we present a novel method of partitioning the bioinformatics data using consensus fuzzy clustering, where the recently proposed fuzzy clustering techniques are exploited. Second, we prepare three high quality subsets of all available indices. Superiority of the consensus fuzzy clustering method is demonstrated quantitatively, visually and statistically by comparing it with the previously proposed hierarchical clustered results. The processed AAindex1 database, supplementary material and the software are available at http://sysbio.icm.edu.pl/aaindex/ .
High Density Impulse Noise Detection using Fuzzy C-means Algorithm
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Isha Singh
2016-01-01
Full Text Available A new technique for detecting the high density impulse noise from corrupted images using Fuzzy C-means algorithm is proposed. The algorithm is iterative in nature and preserves more image details in high noise environment. Fuzzy C-means is initially used to cluster the image data. The application of Fuzzy C-means algorithm in the detection phase provides an optimum classification of noisy data and uncorrupted image data so that the pictorial information remains well preserved. Experimental results show that the proposed algorithm significantly outperforms existing well-known techniques. Results show that with the increase in percentage of noise density, the performance of the algorithm is not degraded. Furthermore, the varying window size in the two detection stages provides more efficient results in terms of low false alarm rate and miss detection rate. The simple structure of the algorithm to detect impulse noise makes it useful for various applications like satellite imaging, remote sensing, medical imaging diagnosis and military survillance. After the efficient detection of noise, the existing filtering techniques can be used for the removal of noise.Defence Science Journal, Vol. 66, No. 1, January 2016, pp. 30-36, DOI: http://dx.doi.org/10.14429/dsj.66.8722
Genetic Algorithm Based Hybrid Fuzzy System for Assessing Morningness
Directory of Open Access Journals (Sweden)
Animesh Biswas
2014-01-01
Full Text Available This paper describes a real life case example on the assessment process of morningness of individuals using genetic algorithm based hybrid fuzzy system. It is observed that physical and mental performance of human beings in different time slots of a day are majorly influenced by morningness orientation of those individuals. To measure the morningness of people various self-reported questionnaires were developed by different researchers in the past. Among them reduced version of Morningness-Eveningness Questionnaire is mostly accepted. Almost all of the linguistic terms used in questionnaires are fuzzily defined. So, assessing them in crisp environments with their responses does not seem to be justifiable. Fuzzy approach based research works for assessing morningness of people are very few in the literature. In this paper, genetic algorithm is used to tune the parameters of a Mamdani fuzzy inference model to minimize error with their predicted outputs for assessing morningness of people.
Application of genetic algorithms to tuning fuzzy control systems
Espy, Todd; Vombrack, Endre; Aldridge, Jack
1993-01-01
Real number genetic algorithms (GA) were applied for tuning fuzzy membership functions of three controller applications. The first application is our 'Fuzzy Pong' demonstration, a controller that controls a very responsive system. The performance of the automatically tuned membership functions exceeded that of manually tuned membership functions both when the algorithm started with randomly generated functions and with the best manually-tuned functions. The second GA tunes input membership functions to achieve a specified control surface. The third application is a practical one, a motor controller for a printed circuit manufacturing system. The GA alters the positions and overlaps of the membership functions to accomplish the tuning. The applications, the real number GA approach, the fitness function and population parameters, and the performance improvements achieved are discussed. Directions for further research in tuning input and output membership functions and in tuning fuzzy rules are described.
Parallel algorithms and cluster computing
Hoffmann, Karl Heinz
2007-01-01
This book presents major advances in high performance computing as well as major advances due to high performance computing. It contains a collection of papers in which results achieved in the collaboration of scientists from computer science, mathematics, physics, and mechanical engineering are presented. From the science problems to the mathematical algorithms and on to the effective implementation of these algorithms on massively parallel and cluster computers we present state-of-the-art methods and technology as well as exemplary results in these fields. This book shows that problems which seem superficially distinct become intimately connected on a computational level.
Directory of Open Access Journals (Sweden)
Yan Hong Chen
2016-01-01
Full Text Available This paper proposes a new electric load forecasting model by hybridizing the fuzzy time series (FTS and global harmony search algorithm (GHSA with least squares support vector machines (LSSVM, namely GHSA-FTS-LSSVM model. Firstly, the fuzzy c-means clustering (FCS algorithm is used to calculate the clustering center of each cluster. Secondly, the LSSVM is applied to model the resultant series, which is optimized by GHSA. Finally, a real-world example is adopted to test the performance of the proposed model. In this investigation, the proposed model is verified using experimental datasets from the Guangdong Province Industrial Development Database, and results are compared against autoregressive integrated moving average (ARIMA model and other algorithms hybridized with LSSVM including genetic algorithm (GA, particle swarm optimization (PSO, harmony search, and so on. The forecasting results indicate that the proposed GHSA-FTS-LSSVM model effectively generates more accurate predictive results.
A fuzzy logic based clustering strategy for improving vehicular ad-hoc network performance
Indian Academy of Sciences (India)
Ali Çalhan
2015-04-01
This paper aims to improve the clustering of vehicles by using fuzzy logic in Vehicular Ad-Hoc Networks (VANETs) for making the network more robust and scalable. High mobility and scalability are two vital topics to be considered while providing efficient and reliable communication in VANETs. Clustering is of crucial significance in order to cope with the dynamic features of the VANET topologies. Plenty of parameters related to user preferences, network conditions and application requirements such as speed of mobile nodes, distance to cluster head, data rate and signal strength must be evaluated in the cluster head selection process together with the direction parameter for highly dynamic VANET structures. The prominent parameters speed, acceleration, distance and direction information are taken into account as inputs of the proposed cluster head selection algorithm. The simulation results show that developed fuzzy logic (FL) based cluster head selection algorithm (CHSA) has stable performance in various scenarios in VANETs. This study has also shown that the developed CHSAFL satisfies well the highly demanding requirements of both low speed and high speed vehicles on two-way multilane highway
Determination of atomic cluster structure with cluster fusion algorithm
DEFF Research Database (Denmark)
Obolensky, Oleg I.; Solov'yov, Ilia; Solov'yov, Andrey V.
2005-01-01
We report an efficient scheme of global optimization, called cluster fusion algorithm, which has proved its reliability and high efficiency in determination of the structure of various atomic clusters.......We report an efficient scheme of global optimization, called cluster fusion algorithm, which has proved its reliability and high efficiency in determination of the structure of various atomic clusters....
Boumediene ALLAOUA; Laoufi, Abdellah; Brahim GASBAOUI; Nasri, Abdelfatah; Abdessalam ABDERRAHMANI
2008-01-01
In this paper, an intelligent controller of the DC (Direct current) Motor drive is designed using fuzzy logic-genetic algorithms optimization. First, a controller is designed according to fuzzy rules such that the systems are fundamentally robust. To obtain the globally optimal values, parameters of the fuzzy controller are improved by genetic algorithms optimization model. Computer MATLAB work space demonstrate that the fuzzy controller associated to the genetic algorithms approach became ve...
Cognitive analysis of multiple sclerosis utilizing fuzzy cluster means
Directory of Open Access Journals (Sweden)
Imianvan Anthony Agboizebeta
2012-01-01
Full Text Available Multiple sclerosis, often called MS, is a disease that affects the central nervous system (the brain and spinal cord. Myelin provides insulation for nerve cells improves the conduction of impulses along the nerves and is important for maintaining the health of the nerves. In multiple sclerosis, inflammation causes the myelin to disappear. Genetic factors, environmental issues and viral infection may also play a role in developing the disease. Ms is characterized by life threatening symptoms such as; loss of balance, hearing problem and depression. The application of Fuzzy Cluster Means (FCM or Fuzzy CMean analysis to the diagnosis of different forms of multiple sclerosis is the focal point of this paper. Application of cluster analysis involves a sequence of methodological and analytical decision steps that enhances the quality and meaning of the clusters produced. Uncertainties associated with analysis of multiple sclerosis test data are eliminated by the system
Cognitive analysis of multiple sclerosis utilizing fuzzy cluster means
Directory of Open Access Journals (Sweden)
Imianvan Anthony Agboizebeta
2012-02-01
Full Text Available Multiple sclerosis, often called MS, is a disease that affects the central nervous system (the brain andspinal cord. Myelin provides insulation for nerve cells improves the conduction of impulses along thenerves and is important for maintaining the health of the nerves. In multiple sclerosis, inflammationcauses the myelin to disappear. Genetic factors, environmental issues and viral infection may alsoplay a role in developing the disease. Ms is characterized by life threatening symptoms such as; loss ofbalance, hearing problem and depression. The application of Fuzzy Cluster Means (FCM or Fuzzy CMeananalysis to the diagnosis of different forms of multiple sclerosis is the focal point of this paper.Application of cluster analysis involves a sequence of methodological and analytical decision stepsthat enhances the quality and meaning of the clusters produced. Uncertainties associated withanalysis of multiple sclerosis test data are eliminated by the system
A New Fuzzy Approach for Dynamic Load Balancing Algorithm
Karimi, Abbas; Jantan, Adznan b; Ramli, A R; Saripan, M Iqbal b
2009-01-01
Load balancing is the process of improving the Performance of a parallel and distributed system through is distribution of load among the processors [1-2]. Most of the previous work in load balancing and distributed decision making in general, do not effectively take into account the uncertainty and inconsistency in state information but in fuzzy logic, we have advantage of using crisps inputs. In this paper, we present a new approach for implementing dynamic load balancing algorithm with fuzzy logic, which can face to uncertainty and inconsistency of previous algorithms, further more our algorithm shows better response time than round robin and randomize algorithm respectively 30.84 percent and 45.45 percent.
A New Fuzzy Approach for Dynamic Load Balancing Algorithm
Directory of Open Access Journals (Sweden)
Abbas Karimi
2009-10-01
Full Text Available Load balancing is the process of improving the Performance of a parallel and distributed system through is distribution of load among the processors [1-2]. Most of the previous work in load balancing and distributed decision making in general, do not effectively take into account the uncertainty and inconsistency in state information but in fuzzy logic, we have advantage of using crisps inputs. In this paper,we present a new approach for implementing dynamic load balancing algorithm with fuzzy logic, which can face to uncertainty and inconsistency of previous algorithms, further more our algorithm shows better response time than round robin and randomize algorithm respectively 30.84% and 45.45%.
A Geometric Fuzzy-Based Approach for Airport Clustering
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Maria Nadia Postorino
2014-01-01
Full Text Available Airport classification is a common need in the air transport field due to several purposes—such as resource allocation, identification of crucial nodes, and real-time identification of substitute nodes—which also depend on the involved actors’ expectations. In this paper a fuzzy-based procedure has been proposed to cluster airports by using a fuzzy geometric point of view according to the concept of unit-hypercube. By representing each airport as a point in the given reference metric space, the geometric distance among airports—which corresponds to a measure of similarity—has in fact an intrinsic fuzzy nature due to the airport specific characteristics. The proposed procedure has been applied to a test case concerning the Italian airport network and the obtained results are in line with expectations.
An Improved Weighted Clustering Algorithm in MANET
Institute of Scientific and Technical Information of China (English)
WANG Jin; XU Li; ZHENG Bao-yu
2004-01-01
The original clustering algorithms in Mobile Ad hoc Network (MANET) are firstly analyzed in this paper.Based on which, an Improved Weighted Clustering Algorithm (IWCA) is proposed. Then, the principle and steps of our algorithm are explained in detail, and a comparison is made between the original algorithms and our improved method in the aspects of average cluster number, topology stability, clusterhead load balance and network lifetime. The experimental results show that our improved algorithm has the best performance on average.
AN ALGORITHM FOR GENERATING SINGLE DIMENSIONAL FUZZY ASSOCIATION RULE MINING
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Rolly Intan
2006-01-01
Full Text Available Association rule mining searches for interesting relationship among items in a large data set. Market basket analysis, a typical example of association rule mining, analyzes buying habit of customers by finding association between the different items that customers put in their shopping cart (basket. Apriori algorithm is an influential algorithm for mining frequent itemset for generating association rules. For some reasons, Apriori algorithm is not based on human intuitive. To provide a more human-based concept, this paper proposes an alternative algorithm for generating the association rule by utilizing fuzzy sets in the market basket analysis.
SURVEY ON CLUSTERING ALGORITHM AND SIMILARITY MEASURE FOR CATEGORICAL DATA
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S. Anitha Elavarasi
2014-01-01
Full Text Available Learning is the process of generating useful information from a huge volume of data. Learning can be either supervised learning (e.g. classification or unsupervised learning (e.g. Clustering Clustering is the process of grouping a set of physical objects into classes of similar object. Objects in real world consist of both numerical and categorical data. Categorical data are not analyzed as numerical data because of the absence of inherit ordering. This paper describes about ten different clustering algorithms, its methodology and the factors influencing its performance. Each algorithm is evaluated using real world datasets and its pro and cons are specified. The various similarity / dissimilarity measure applied to categorical data and its performance is also discussed. The time complexity defines the amount of time taken by an algorithm to perform the elementary operation. The time complexity of various algorithms are discussed and its performance on real world data such as mushroom, zoo, soya bean, cancer, vote, car and iris are measured. In this survey Cluster Accuracy and Error rate for four different clustering algorithm (K-modes, fuzzy K-modes, ROCK and Squeezer, two different similarity measure (DISC and Overlap and DILCA applied for hierarchy and partition algorithm are evaluated.
Novel effective fuzzy diffusion algorithm for noise removal
Zhang, Yingtao; Cheng, Heng-Da; Huang, Jianhua; Tang, Xianglong
2010-12-01
Anisotropic diffusion is widely used for noise reduction. The performance of anisotropic diffusion, in general, depends on the shape of the energy surface. The partial differential equation model is established and analyzed in the continuous domain while is implemented in the discrete domain. Therefore, the anisotropic diffusion bears some fuzziness due to the approximation. We present a novel noise removal algorithm based on fuzzy logic and anisotropic diffusion theory. The experimental results demonstrate that the proposed method has the advantage of maximizing noise reduction and preserving fine details of the images. In addition, the method can enhance the contrast of the images well.
A new cluster algorithm for graphs
Dongen, S. van
1998-01-01
A new cluster algorithm for graphs called the emph{Markov Cluster algorithm ($MCL$ algorithm) is introduced. The graphs may be both weighted (with nonnegative weight) and directed. Let~$G$~be such a graph. The $MCL$ algorithm simulates flow in $G$ by first identifying $G$ in a canonical way with
An improved algorithm for clustering gene expression data.
Bandyopadhyay, Sanghamitra; Mukhopadhyay, Anirban; Maulik, Ujjwal
2007-11-01
Recent advancements in microarray technology allows simultaneous monitoring of the expression levels of a large number of genes over different time points. Clustering is an important tool for analyzing such microarray data, typical properties of which are its inherent uncertainty, noise and imprecision. In this article, a two-stage clustering algorithm, which employs a recently proposed variable string length genetic scheme and a multiobjective genetic clustering algorithm, is proposed. It is based on the novel concept of points having significant membership to multiple classes. An iterated version of the well-known Fuzzy C-Means is also utilized for clustering. The significant superiority of the proposed two-stage clustering algorithm as compared to the average linkage method, Self Organizing Map (SOM) and a recently developed weighted Chinese restaurant-based clustering method (CRC), widely used methods for clustering gene expression data, is established on a variety of artificial and publicly available real life data sets. The biological relevance of the clustering solutions are also analyzed.
Energy Technology Data Exchange (ETDEWEB)
Mackey, Lester [Department of Statistics, Stanford University,Stanford, CA 94305 (United States); Nachman, Benjamin [Department of Physics, Stanford University,Stanford, CA 94305 (United States); SLAC National Accelerator Laboratory, Stanford University,2575 Sand Hill Rd, Menlo Park, CA 94025 (United States); Schwartzman, Ariel [SLAC National Accelerator Laboratory, Stanford University,2575 Sand Hill Rd, Menlo Park, CA 94025 (United States); Stansbury, Conrad [Department of Physics, Stanford University,Stanford, CA 94305 (United States)
2016-06-01
Collimated streams of particles produced in high energy physics experiments are organized using clustering algorithms to form jets. To construct jets, the experimental collaborations based at the Large Hadron Collider (LHC) primarily use agglomerative hierarchical clustering schemes known as sequential recombination. We propose a new class of algorithms for clustering jets that use infrared and collinear safe mixture models. These new algorithms, known as fuzzy jets, are clustered using maximum likelihood techniques and can dynamically determine various properties of jets like their size. We show that the fuzzy jet size adds additional information to conventional jet tagging variables in boosted topologies. Furthermore, we study the impact of pileup and show that with some slight modifications to the algorithm, fuzzy jets can be stable up to high pileup interaction multiplicities.
Fuzzy Algorithm for Power Transformer Diagnostics
Directory of Open Access Journals (Sweden)
Nitin K. Dhote
2013-01-01
Full Text Available Dissolved gas analysis (DGA of transformer oil has been one of the most reliable techniques to detect the incipient faults. Many conventional DGA methods have been developed to interpret DGA results obtained from gas chromatography. Although these methods are widely used in the world, they sometimes fail to diagnose, especially when DGA results fall outside conventional methods codes or when more than one fault exist in the transformer. To overcome these limitations, the fuzzy inference system (FIS is proposed. Two hundred different cases are used to test the accuracy of various DGA methods in interpreting the transformer condition.
Castro, Alfonso; Rey, Alberto; Boveda, Carmen; Arcay, Bernardino; Sanjurjo, Pedro
2016-01-01
The detection of pulmonary nodules is one of the most studied problems in the field of medical image analysis due to the great difficulty in the early detection of such nodules and their social impact. The traditional approach involves the development of a multistage CAD system capable of informing the radiologist of the presence or absence of nodules. One stage in such systems is the detection of ROI (regions of interest) that may be nodules in order to reduce the space of the problem. This paper evaluates fuzzy clustering algorithms that employ different classification strategies to achieve this goal. After characterising these algorithms, the authors propose a new algorithm and different variations to improve the results obtained initially. Finally it is shown as the most recent developments in fuzzy clustering are able to detect regions that may be nodules in CT studies. The algorithms were evaluated using helical thoracic CT scans obtained from the database of the LIDC (Lung Image Database Consortium).
Uncovering and testing the fuzzy clusters based on lumped Markov chain in complex network.
Jing, Fan; Jianbin, Xie; Jinlong, Wang; Jinshuai, Qu
2013-01-01
Identifying clusters, namely groups of nodes with comparatively strong internal connectivity, is a fundamental task for deeply understanding the structure and function of a network. By means of a lumped Markov chain model of a random walker, we propose two novel ways of inferring the lumped markov transition matrix. Furthermore, some useful results are proposed based on the analysis of the properties of the lumped Markov process. To find the best partition of complex networks, a novel framework including two algorithms for network partition based on the optimal lumped Markovian dynamics is derived to solve this problem. The algorithms are constructed to minimize the objective function under this framework. It is demonstrated by the simulation experiments that our algorithms can efficiently determine the probabilities with which a node belongs to different clusters during the learning process and naturally supports the fuzzy partition. Moreover, they are successfully applied to real-world network, including the social interactions between members of a karate club.
Fuzzy Temporal Clustering Approach for E-Commerce Websites
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Sudhamathy G.
2012-07-01
Full Text Available In this paper a novel approach for clustering of web logs data and to predict intelligent recommendations on the E-Commerce web sites is proposed so as to improve the marketing strategy and to improve customer loyalty. Fuzzy Temporal Clustering Approach (FTCA performs clustering of the web site visitors and the web site pages based on the frequency of visit and time spent. Time plays a crucial role in the analysis of web usage. Hence these clusters are studied over a period of time to study the migration behaviour of the users and the pages across periods. Such a study can provide intelligentrecommendations for the E-Commerce web sites that focus on specific product recommendations and behavioural targeting. Experimental evaluation of the method has proved that this approach FTCA is most efficient, easy to use and a useful clustering approach.
A hybrid differential evolution algorithm to vehicle routing problem with fuzzy demands
Erbao, Cao; Mingyong, Lai
2009-09-01
In this paper, the vehicle routing problem with fuzzy demands (VRPFD) is considered, and a fuzzy chance constrained program model is designed, based on fuzzy credibility theory. Then stochastic simulation and differential evolution algorithm are integrated to design a hybrid intelligent algorithm to solve the fuzzy chance constrained program model. Moreover, the influence of the dispatcher preference index on the final objective of the problem is discussed using stochastic simulation, and the best value of the dispatcher preference index is obtained.
Xu, Zeshui
2014-01-01
This book provides the readers with a thorough and systematic introduction to hesitant fuzzy theory. It presents the most recent research results and advanced methods in the field. These includes: hesitant fuzzy aggregation techniques, hesitant fuzzy preference relations, hesitant fuzzy measures, hesitant fuzzy clustering algorithms and hesitant fuzzy multi-attribute decision making methods. Since its introduction by Torra and Narukawa in 2009, hesitant fuzzy sets have become more and more popular and have been used for a wide range of applications, from decision-making problems to cluster analysis, from medical diagnosis to personnel appraisal and information retrieval. This book offers a comprehensive report on the state-of-the-art in hesitant fuzzy sets theory and applications, aiming at becoming a reference guide for both researchers and practitioners in the area of fuzzy mathematics and other applied research fields (e.g. operations research, information science, management science and engineering) chara...
CAC Algorithm Based on Fuzzy Logic
Directory of Open Access Journals (Sweden)
Ľubomír DOBOŠ
2009-05-01
Full Text Available Quality of Service (QoS represent one ofmajor parameters that describe mobile wirelesscommunication systems. Thanks growing popularity ofmobile communication in last years, there is anincreasing expansion of connection admission controlschemes (CAC that plays important role in QoSdelivering in terms of connection blocking probability,connection dropping probability, data loss rate andsignal quality.With expansion of services provided by the mobilenetworks growing the requirements to QoS andtogether growing requirements to CAC schemes.Therefore, still more sophisticated CAC schemes arerequired to guarantee the QoS. This paper containsshort introduction into division of connectionadmission control schemes and presents thresholdoriented CAC scheme with fuzzy logic used foradaptation of the threshold value.
Fuzzy clustering-based segmented attenuation correction in whole-body PET
Zaidi, H; Boudraa, A; Slosman, DO
2001-01-01
Segmented-based attenuation correction is now a widely accepted technique to reduce noise contribution of measured attenuation correction. In this paper, we present a new method for segmenting transmission images in positron emission tomography. This reduces the noise on the correction maps while still correcting for differing attenuation coefficients of specific tissues. Based on the Fuzzy C-Means (FCM) algorithm, the method segments the PET transmission images into a given number of clusters to extract specific areas of differing attenuation such as air, the lungs and soft tissue, preceded by a median filtering procedure. The reconstructed transmission image voxels are therefore segmented into populations of uniform attenuation based on the human anatomy. The clustering procedure starts with an over-specified number of clusters followed by a merging process to group clusters with similar properties and remove some undesired substructures using anatomical knowledge. The method is unsupervised, adaptive and a...
Moini, A
2002-01-01
In this paper, genetic algorithms are used in the design and robustification various mo el-ba ed/non-model-based fuzzy-logic controllers for robotic manipulators. It is demonstrated that genetic algorithms provide effective means of designing the optimal set of fuzzy rules as well as the optimal domains of associated fuzzy sets in a new class of model-based-fuzzy-logic controllers. Furthermore, it is shown that genetic algorithms are very effective in the optimal design and robustification of non-model-based multivariable fuzzy-logic controllers for robotic manipulators.
Fuzzy Controllers Based Multipath Routing Algorithm in MANET
Pi, Shangchao; Sun, Baolin
Mobile ad hoc networks (MANETs) consist of a collection of wireless mobile nodes which dynamically exchange data among themselves without the reliance on a fixed base station or a wired backbone network. Due to the limited transmission range of wireless network nodes, multiple hops are usually needed for a node to exchange information with any other node in the network. Multipath routing allows the establishment of multiple paths between a single source and single destination node. The multipath routing in mobile ad hoc networks is difficult because the network topology may change constantly, and the available alternative path is inherently unreliable. This paper introduces a fuzzy controllers based multipath routing algorithm in MANET (FMRM). The key idea of FMRM algorithm is to construct the fuzzy controllers with the help to reduce reconstructions in the ad hoc network. The simulation results show that the proposed approach is effective and efficient in applications to the MANETs. It is an available approach to multipath routing decision.
Directory of Open Access Journals (Sweden)
D. A. Viattchenin
2009-01-01
Full Text Available A method for constructing a subset of labeled objects which is used in a heuristic algorithm of possible clusterization with partial training is proposed in the paper. The method is based on data preprocessing by the heuristic algorithm of possible clusterization using a transitive closure of a fuzzy tolerance. Method efficiency is demonstrated by way of an illustrative example.
Application of Fuzzy Clustering in Modeling of a Water Hydraulics System
DEFF Research Database (Denmark)
Zhou, Jianjun; Kroszynski, Uri
2000-01-01
This article presents a case study of applying fuzzy modeling techniques for a water hydraulics system. The obtained model is intended to provide a basis for model-based control of the system. Fuzzy clustering is used for classifying measured input-output data points into partitions. The fuzzy mo...
A Rough Set based Gene Expression Clustering Algorithm
Directory of Open Access Journals (Sweden)
J. J. Emilyn
2011-01-01
Full Text Available Problem statement: Microarray technology helps in monitoring the expression levels of thousands of genes across collections of related samples. Approach: The main goal in the analysis of large and heterogeneous gene expression datasets was to identify groups of genes that get expressed in a set of experimental conditions. Results: Several clustering techniques have been proposed for identifying gene signatures and to understand their role and many of them have been applied to gene expression data, but with partial success. The main aim of this work was to develop a clustering algorithm that would successfully indentify gene patterns. The proposed novel clustering technique (RCGED provides an efficient way of finding the hidden and unique gene expression patterns. It overcomes the restriction of one object being placed in only one cluster. Conclusion/Recommendations: The proposed algorithm is termed intelligent because it automatically determines the optimum number of clusters. The proposed algorithm was experimented with colon cancer dataset and the results were compared with Rough Fuzzy K Means algorithm.
Bilateral Filtering using Modified Fuzzy Clustering for Image Denoising
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G.Vijaya,
2011-01-01
Full Text Available This paper presents a novel bilateral filtering using weighed fcm algorithm based on Gaussian kernel unction for image manipulations such as segmentation and denoising . Our proposed bilateral filteringconsists of the standard bilateral filter and the original Euclidean distance is replaced by a kernel – induced distance in the algorithm. We have applied the proposed filtering for image denoising with both the impulse and Gaussian random noise, which achieves better results than the bilateral filtering based denoising approaches, the Perona-Maliks anisotropic diffusion filter, the fuzzy vector median filter and the Non-Local Means filter.
Evolutionary Based Type-2 Fuzzy Routing Protocol for Clustered Wireless Sensor
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Maryam Salehi
2016-06-01
Full Text Available Power management is an important issue in wireless sensor network as the sensor nodes are battery-operated devices. For energy efficient data transmission, many routing protocols have been proposed. To achieve energy efficiency in wireless sensor networks, Clustering is an effective approach. In clustering routing protocol, Cluster heads are selected among all nodes within the wireless sensor networkand clusters are formed by assigning each node to the nearest cluster.Energy efficiency, network lifetime and uncertainties are the main drawbacks in clustering routing protocols.In this paper, a new clustering routing protocol named T2FLSBA is introduced to select optimal cluster heads. The proposed protocol is based on type-2 fuzzy logic system. To achieve the best performance based on the application, its parameters are tuned based on bat algorithm.The three important factors- residual energy, the density of neighbour sensor nodes and the distance to sink are taken into consideration as inputs of T2FLSBA protocol to compute the probability of a node to be a candidate cluster head. The simulation results show that the proposed routing protocol outperforms the existing clustering routing protocols in terms of prolonging the network lifetime and energy consumption of sensor nodes.
PHONETIC CLASSIFICATION BY ADAPTIVE NETWORK BASED FUZZY INFERENCE SYSTEM AND SUBTRACTIVE CLUSTERING
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Samiya Silarbi
2014-09-01
Full Text Available This paper presents the application of Adaptive Network Based Fuzzy Inference System ANFIS on speech recognition. The primary tasks of fuzzy modeling are structure identification and parameter optimization, the former determines the numbers of membership functions and fuzzy if-then rules while the latter identifies a feasible set of parameters under the given structure. However, the increase of input dimension, rule numbers will have an exponential growth and there will cause problem of “rule disaster”. Thus, determination of an appropriate structure becomes an important issue where subtractive clustering is applied to define an optimal initial structure and obtain small number of rules. The appropriate learning algorithm is performed on TIMIT speech database supervised type, a pre-processing of the acoustic signal and extracting the coefficients MFCCs parameters relevant to the recognition system. Finally, hybrid learning combines the gradient decent and least square estimation LSE of parameters network. The results obtained show the effectiveness of the method in terms of recognition rate and number of fuzzy rules generated.
Frequent Pattern Mining Algorithms for Data Clustering
DEFF Research Database (Denmark)
Zimek, Arthur; Assent, Ira; Vreeken, Jilles
2014-01-01
that frequent pattern mining was at the cradle of subspace clustering—yet, it quickly developed into an independent research field. In this chapter, we discuss how frequent pattern mining algorithms have been extended and generalized towards the discovery of local clusters in high-dimensional data......Discovering clusters in subspaces, or subspace clustering and related clustering paradigms, is a research field where we find many frequent pattern mining related influences. In fact, as the first algorithms for subspace clustering were based on frequent pattern mining algorithms, it is fair to say....... In particular, we discuss several example algorithms for subspace clustering or projected clustering as well as point out recent research questions and open topics in this area relevant to researchers in either clustering or pattern mining...
PERFORMANCE ANALYSIS OF IMAGE COMPRESSION USING FUZZY LOGIC ALGORITHM
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Rohit Kumar Gangwar
2014-04-01
Full Text Available With the increase in demand, product of multimedia is increasing fast and thus contributes to insufficient network bandwidth and memory storage. Therefore image compression is more significant for reducing data redundancy for save more memory and transmission bandwidth. An efficient compression technique has been proposed which combines fuzzy logic with that of Huffman coding. While normalizing image pixel, each value of pixel image belonging to that image foreground are characterized and interpreted. The image is sub divided into pixel which is then characterized by a pair of set of approximation. Here encoding represent Huffman code which is statistically independent to produce more efficient code for compression and decoding represents rough fuzzy logic which is used to rebuilt the pixel of image. The method used here are rough fuzzy logic with Huffman coding algorithm (RFHA. Here comparison of different compression techniques with Huffman coding is done and fuzzy logic is applied on the Huffman reconstructed image. Result shows that high compression rates are achieved and visually negligible difference between compressed images and original images.
Halder, Amiya
2012-01-01
This paper proposes a Genetic Algorithm based segmentation method that can automatically segment gray-scale images. The proposed method mainly consists of spatial unsupervised grayscale image segmentation that divides an image into regions. The aim of this algorithm is to produce precise segmentation of images using intensity information along with neighborhood relationships. In this paper, Fuzzy Hopfield Neural Network (FHNN) clustering helps in generating the population of Genetic algorithm which there by automatically segments the image. This technique is a powerful method for image segmentation and works for both single and multiple-feature data with spatial information. Validity index has been utilized for introducing a robust technique for finding the optimum number of components in an image. Experimental results shown that the algorithm generates good quality segmented image.
Fuzzy support vector machines based on linear clustering
Xiong, Shengwu; Liu, Hongbing; Niu, Xiaoxiao
2005-10-01
A new Fuzzy Support Vector Machines (FSVMs) based on linear clustering is proposed in this paper. Its concept comes from the idea of linear clustering, selecting the data points near to the preformed hyperplane, which is formed on the training set including one positive and one negative training samples respectively. The more important samples near to the preformed hyperplane are selected by linear clustering technique, and the new FSVMs are formed on the more important data set. It integrates the merit of two kinds of FSVMs. The membership functions are defined using the relative distance between the data points and the preformed hyperplane during the training process. The fuzzy membership decision functions of multi-class FSVMs adopt the minimal value of all the decision functions of two-class FSVMs. To demonstrate the superiority of our methods, the benchmark data sets of machines learning databases are selected to verify the proposed FSVMs. The experimental results indicate that the proposed FSVMs can reduce the training data and running time, and its recognition rate is greater than or equal to that of FSVMs through selecting a suitable linear clustering parameter.
Diagnosis support using Fuzzy Cognitive Maps combined with Genetic Algorithms.
Georgopoulos, Voula C; Stylios, Chrysotomos D
2009-01-01
A new hybrid modeling methodology to support medical diagnosis decisions is developed here. It extends previous work on Competitive Fuzzy Cognitive Maps for Medical Diagnosis Support Systems by complementing them with Genetic Algorithms Methods for concept interaction. The synergy of these methodologies is accomplished by a new proposed algorithm that leads to more dependable Advanced Medical Diagnosis Support Systems that are suitable to handle situations where the decisions are not clearly distinct. The technique developed here is applied successfully to model and test a differential diagnosis problem from the speech pathology area for the diagnosis of language impairments.
Multivariate image segmentation with cluster size insensitive Fuzzy C-means
Noordam, J.C.; Broek, van den W.H.A.M.; Buydens, L.M.C.
2002-01-01
This paper describes a technique to overcome the sensitivity of fuzzy C-means clustering for unequal cluster sizes in multivariate images. As FCM tends to balance the number of points in each cluster, cluster centres of smaller clusters are drawn to larger adjacent clusters. In order to overcome
Introduction to Cluster Monte Carlo Algorithms
Luijten, E.
This chapter provides an introduction to cluster Monte Carlo algorithms for classical statistical-mechanical systems. A brief review of the conventional Metropolis algorithm is given, followed by a detailed discussion of the lattice cluster algorithm developed by Swendsen and Wang and the single-cluster variant introduced by Wolff. For continuum systems, the geometric cluster algorithm of Dress and Krauth is described. It is shown how their geometric approach can be generalized to incorporate particle interactions beyond hardcore repulsions, thus forging a connection between the lattice and continuum approaches. Several illustrative examples are discussed.
Using memristor crossbar structure to implement a novel adaptive real time fuzzy modeling algorithm
Afrakoti, Iman Esmaili Paeen; Shouraki, Saeed Bagheri; Merrikhbayat, Farnood
2013-01-01
Although fuzzy techniques promise fast meanwhile accurate modeling and control abilities for complicated systems, different difficulties have been re-vealed in real situation implementations. Usually there is no escape of it-erative optimization based on crisp domain algorithms. Recently memristor structures appeared promising to implement neural network structures and fuzzy algorithms. In this paper a novel adaptive real-time fuzzy modeling algorithm is proposed which uses active learning me...
A Novel Research on Rough Clustering Algorithm
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Tao Qu
2014-01-01
Full Text Available The aim of this study is focusing the issue of traditional clustering algorithm subjects to data space distribution influence, a novel clustering algortihm combined with rough set theory is employed to the normal clustering. The proposed rough clustering algorithm takes the condition attributes and decision attributes displayed in the information table as the consistency principle, meanwhile it takes the data supercubic and information entropy to realize data attribute shortcutting and discretizing. Based on above discussion, by applying assemble feature vector addition principle computiation only one scanning information table can realize clustering for the data subject. Experiments reveal that the proposed algorithm is efficient and feasible.
The fuzzy logic algorithm has the ability to describe knowledge in a descriptive human-like manner in the form of simple rules using linguistic variables, and provides a new way of modeling uncertain or naturally fuzzy hydrological processes like non-linear rainfall-runoff relationships. Fuzzy infe...
Fuzzy context-free languages. Part 2: Recognition and parsing algorithms
Asveld, Peter R.J.
2005-01-01
In a companion paper [P.R.J. Asveld, Fuzzy context-free languagesPart 1: Generalized fuzzy context-free grammars, Theoret. Comput. Sci., (2005).] we used fuzzy context-free grammars in order to model grammatical errors resulting in erroneous inputs for robust recognizing and parsing algorithms for f
A fuzzy simulated evolution algorithm for integrated manufacturing system design
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Michael Mutingi
2013-04-01
Full Text Available Integrated cell formation and layout (CFLP is an extended application of the group technology philosophy in which machine cells and cell layout are addressed simultaneously. The aim of this technological innovation is to improve both productivity and flexibility in modern manufacturing industry. However, due to its combinatorial complexity, the cell formation and layout problem is best solved by heuristic and metaheuristic approaches. As CFLP is prevalent in manufacturing industry, developing robust and efficient solution methods for the problem is imperative. This study seeks to develop a fuzzy simulated evolution algorithm (FSEA that integrates fuzzy-set theoretic concepts and the philosophy of constructive perturbation and evolution. Deriving from the classical simulated evolution algorithm, the search efficiency of the major phases of the algorithm is enhanced, including initialization, evaluation, selection and reconstruction. Illustrative computational experiments based on existing problem instances from the literature demonstrate the utility and the strength of the FSEA algorithm developed in this study. It is anticipated in this study that the application of the algorithm can be extended to other complex combinatorial problems in industry.
Fuzzy logic-based diagnostic algorithm for implantable cardioverter defibrillators.
Bárdossy, András; Blinowska, Aleksandra; Kuzmicz, Wieslaw; Ollitrault, Jacky; Lewandowski, Michał; Przybylski, Andrzej; Jaworski, Zbigniew
2014-02-01
The paper presents a diagnostic algorithm for classifying cardiac tachyarrhythmias for implantable cardioverter defibrillators (ICDs). The main aim was to develop an algorithm that could reduce the rate of occurrence of inappropriate therapies, which are often observed in existing ICDs. To achieve low energy consumption, which is a critical factor for implantable medical devices, very low computational complexity of the algorithm was crucial. The study describes and validates such an algorithm and estimates its clinical value. The algorithm was based on the heart rate variability (HRV) analysis. The input data for our algorithm were: RR-interval (I), as extracted from raw intracardiac electrogram (EGM), and in addition two other features of HRV called here onset (ONS) and instability (INST). 6 diagnostic categories were considered: ventricular fibrillation (VF), ventricular tachycardia (VT), sinus tachycardia (ST), detection artifacts and irregularities (including extrasystoles) (DAI), atrial tachyarrhythmias (ATF) and no tachycardia (i.e. normal sinus rhythm) (NT). The initial set of fuzzy rules based on the distributions of I, ONS and INST in the 6 categories was optimized by means of a software tool for automatic rule assessment using simulated annealing. A training data set with 74 EGM recordings was used during optimization, and the algorithm was validated with a validation data set with 58 EGM recordings. Real life recordings stored in defibrillator memories were used. Additionally the algorithm was tested on 2 sets of recordings from the PhysioBank databases: MIT-BIH Arrhythmia Database and MIT-BIH Supraventricular Arrhythmia Database. A custom CMOS integrated circuit implementing the diagnostic algorithm was designed in order to estimate the power consumption. A dedicated Web site, which provides public online access to the algorithm, has been created and is available for testing it. The total number of events in our training and validation sets was 132. In
基于半模糊核聚类算法的收视率预测研究%Study on audio rating prediction based on semi-fuzzy kernel clustering algorithm
Institute of Scientific and Technical Information of China (English)
陈青; 薛惠锋; 闫莉
2012-01-01
Audio rating is one of the most important indicators of TV industry, which is of great value to decision making of TV station. According to characters of more impact factors and complexity of variety, a new method of hyper-sphere Support Vector Machine (SVM) multi-class classification based on semi-fuzzy kernel clustering is proposed. The new method defines confusion classes based on semi-fuzzy kernel clustering and sphere support vector machines using the information from boundary so as to improve the performance of classifier efficiently. Experimental results indicate that the new method yields higher precision and speed than classical classification methods.%收视率是电视行业重要的指标之一,对电视机构运营决策具有重要参考价值.针对收视率数据影响因素众多,变化趋势复杂等特点,提出了一种基于半模糊核聚类的超球支持向量机分类方法,基于半模糊核聚类生成模糊类,在其边缘样本信息基础上,利用超球支持向量机进行多类分类,从而有效提高分类器性能.实验表明,该方法比传统方法具有更高的速度和精度.
Elazab, Ahmed; Wang, Changmiao; Jia, Fucang; Wu, Jianhuang; Li, Guanglin; Hu, Qingmao
2015-01-01
An adaptively regularized kernel-based fuzzy C-means clustering framework is proposed for segmentation of brain magnetic resonance images. The framework can be in the form of three algorithms for the local average grayscale being replaced by the grayscale of the average filter, median filter, and devised weighted images, respectively. The algorithms employ the heterogeneity of grayscales in the neighborhood and exploit this measure for local contextual information and replace the standard Euclidean distance with Gaussian radial basis kernel functions. The main advantages are adaptiveness to local context, enhanced robustness to preserve image details, independence of clustering parameters, and decreased computational costs. The algorithms have been validated against both synthetic and clinical magnetic resonance images with different types and levels of noises and compared with 6 recent soft clustering algorithms. Experimental results show that the proposed algorithms are superior in preserving image details and segmentation accuracy while maintaining a low computational complexity.
Directory of Open Access Journals (Sweden)
Ahmed Elazab
2015-01-01
Full Text Available An adaptively regularized kernel-based fuzzy C-means clustering framework is proposed for segmentation of brain magnetic resonance images. The framework can be in the form of three algorithms for the local average grayscale being replaced by the grayscale of the average filter, median filter, and devised weighted images, respectively. The algorithms employ the heterogeneity of grayscales in the neighborhood and exploit this measure for local contextual information and replace the standard Euclidean distance with Gaussian radial basis kernel functions. The main advantages are adaptiveness to local context, enhanced robustness to preserve image details, independence of clustering parameters, and decreased computational costs. The algorithms have been validated against both synthetic and clinical magnetic resonance images with different types and levels of noises and compared with 6 recent soft clustering algorithms. Experimental results show that the proposed algorithms are superior in preserving image details and segmentation accuracy while maintaining a low computational complexity.
Directory of Open Access Journals (Sweden)
Guoying Liu
2015-01-01
Full Text Available This paper presents a variation of the fuzzy local information c-means clustering (FLICM algorithm that provides color texture image clustering. The proposed algorithm incorporates region-level spatial, spectral, and structural information in a novel fuzzy way. The new algorithm, called RFLICM, combines FLICM and region-level Markov random field model (RMRF together to make use of large scale interactions between image patches instead of pixels. RFLICM can overcome the weakness of FLICM when dealing with textured images and at the same time enhances the clustering performance. The major characteristic of RFLICM is the use of a region-level fuzzy factor, aiming to guarantee texture homogeneity and preserve region boundaries. Experiments performed on synthetic and remote sensing images show that RFLICM is effective in providing accuracy to color texture images.
Navigation Algorithm Using Fuzzy Control Method in Mobile Robotics
Directory of Open Access Journals (Sweden)
Cviklovič Vladimír
2016-03-01
Full Text Available The issue of navigation methods is being continuously developed globally. The aim of this article is to test the fuzzy control algorithm for track finding in mobile robotics. The concept of an autonomous mobile robot EN20 has been designed to test its behaviour. The odometry navigation method was used. The benefits of fuzzy control are in the evidence of mobile robot’s behaviour. These benefits are obtained when more physical variables on the base of more input variables are controlled at the same time. In our case, there are two input variables - heading angle and distance, and two output variables - the angular velocity of the left and right wheel. The autonomous mobile robot is moving with human logic.
A neuro-fuzzy controlling algorithm for wind turbine
Energy Technology Data Exchange (ETDEWEB)
Li Lin [Tampere Univ. of Technology (Finland); Eriksson, J.T. [Tampere Univ. of Technology (Finland)
1995-12-31
The wind turbine control system is stochastic and nonlinear, offering a demanding field for different control methods. An improved and efficient controller will have great impact on the cost-effectiveness of the technology. In this article, a design method for a self-organizing fuzzy controller is discussed, which combines two popular computational intelligence techniques, neural networks and fuzzy logic. Based on acquisited dynamic parameters of the wind, it can effectively predict wind changes in speed and direction. Maximum power can always be extracted from the kinetic energy of the wind. Based on the stimulating experiments applying nonlinear dynamics to a `Variable Speed Fixed Angle` wind turbine, it is demonstrated that the proposed control model 3rd learning algorithm provide a predictable, stable and accurate performance. The robustness of the controller to system parameter variations and measurement disturbances is also discussed. (author)
Directory of Open Access Journals (Sweden)
D. Vydeki
2013-01-01
Full Text Available Intrusion Detection System (IDS provides additional security for the most vulnerable Mobile Adhoc Networks (MANET. Use of Fuzzy Inference System (FIS in the design of IDS is proven to be efficient in detecting routing attacks in MANETs. Clustering is a vital means in the detection process of FIS based hybrid IDS. This study describes the design of such a system to detect black hole attack in MANET that uses Adhoc On-Demand Distance Vector (AODV routing protocol. It analyses the effect of two clustering algorithms and also prescribes the suitable clustering algorithm for the above-mentioned IDS. MANETs with various traffic scenarios were simulated and the data set required for the IDS is extracted. A hybrid IDS is designed using Sugeno type-2 FIS to detect black hole attack. From the experimental results, it is derived that the subtractive clustering algorithm produces 97% efficient detection while FCM offers 91%. It has been found that the subtractive clustering algorithm is more fit and efficient than the Fuzzy C-Means clustering (FCM for the FIS based detection system.
Detection of Microcalcification in Mammograms Using Wavelet Transform and Fuzzy Shell Clustering
Balakumaran, T; Shankar, C Gowri
2010-01-01
Microcalcifications in mammogram have been mainly targeted as a reliable earliest sign of breast cancer and their early detection is vital to improve its prognosis. Since their size is very small and may be easily overlooked by the examining radiologist, computer-based detection output can assist the radiologist to improve the diagnostic accuracy. In this paper, we have proposed an algorithm for detecting microcalcification in mammogram. The proposed microcalcification detection algorithm involves mammogram quality enhancement using multirresolution analysis based on the dyadic wavelet transform and microcalcification detection by fuzzy shell clustering. It may be possible to detect nodular components such as microcalcification accurately by introducing shape information. The effectiveness of the proposed algorithm for microcalcification detection is confirmed by experimental results.
Implementation of Clustering Algorithms for real datasets in Medical Diagnostics using MATLAB
Directory of Open Access Journals (Sweden)
B. Venkataramana
2017-03-01
Full Text Available As in the medical field, for one disease there require samples given by diagnosis. The samples will be analyzed by a doctor or a pharmacist. As the no. of patients increases their samples also increases, there require more time to analyze samples for deciding the stage of the disease. To analyze the sample every time requires a skilled person. The samples can be classified by applying them to clustering algorithms. Data clustering has been considered as the most important raw data analysis method used in data mining technology. Most of the clustering techniques proved their efficiency in many applications such as decision making systems, medical sciences, earth sciences etc. Partition based clustering is one of the main approach in clustering. There are various algorithms of data clustering, every algorithm has its own advantages and disadvantages. This work reports the results of classification performance of three such widely used algorithms namely K-means (KM, Fuzzy c-means and Fuzzy Possibilistic c-Means (FPCM clustering algorithms. To analyze these algorithms three known data sets from UCI machine learning repository are taken such as thyroid data, liver and wine. The efficiency of clustering output is compared with the classification performance, percentage of correctness. The experimental results show that K-means and FCM give same performance for liver data. And FCM and FPCM are giving same performance for thyroid and wine data. FPCM has more efficient classification performance in all the given data sets.
Algorithm for Spatial Clustering with Obstacles
El-Sharkawi, Mohamed E
2009-01-01
In this paper, we propose an efficient clustering technique to solve the problem of clustering in the presence of obstacles. The proposed algorithm divides the spatial area into rectangular cells. Each cell is associated with statistical information that enables us to label the cell as dense or non-dense. We also label each cell as obstructed (i.e. intersects any obstacle) or non-obstructed. Then the algorithm finds the regions (clusters) of connected, dense, non-obstructed cells. Finally, the algorithm finds a center for each such region and returns those centers as centers of the relatively dense regions (clusters) in the spatial area.
A fuzzy-clustering analysis based phonetic tied-mixture HMM
Institute of Scientific and Technical Information of China (English)
XU Xianghua; ZHU Jie; GUO Qiang
2005-01-01
To efficiently decrease the size of parameters and improve the robustness of parameters training, a fuzzy clustering based phonetic tied-mixture model, FPTM, is presented.The Gaussian codebook of FPTM is synthesized from Gaussian components belonging to the same root node in phonetic decision tree. Fuzzy clustering method is further used for FPTM covariance sharing. Experimental results show that compared with the conventional PTM with approximately the same parameters size, FPTM decrease the size of Gaussian weights by 77.59% and increases word accuracy by 7.92%, which proves Gaussian fuzzy clustering is efficient. Compared with FPTM, covariance-shared FPTM decreases word error rate by 1.14% , which proves the combined fuzzy clustering for both Gaussian and covariance is superior to Gaussian fuzzy clustering alone.
Directory of Open Access Journals (Sweden)
Ashraf Afifi
2012-07-01
Full Text Available In this paper we present a hybrid approach based on combining fuzzy k-means clustering, seed region growing, and sensitivity and specificity algorithms to measure gray (GM and white matter (WM tissue. The proposed algorithm uses intensity and anatomic information for segmenting of MRIs into different tissue classes, especially GM and WM. It starts by partitioning the image into different clusters using fuzzy k-means clustering. The centers of these clusters are the input to the region growing (SRG method for creating the closed regions. The outputs of SRG technique are fed to sensitivity and specificity algorithm to merge the similar regions in one segment. The proposed algorithm is applied to challenging applications: gray matter/white matter segmentation in magnetic resonance image (MRI datasets. The experimental results show that the proposed technique produces accurate and stable results.
Directory of Open Access Journals (Sweden)
Boumediene ALLAOUA
2008-12-01
Full Text Available In this paper, an intelligent controller of the DC (Direct current Motor drive is designed using fuzzy logic-genetic algorithms optimization. First, a controller is designed according to fuzzy rules such that the systems are fundamentally robust. To obtain the globally optimal values, parameters of the fuzzy controller are improved by genetic algorithms optimization model. Computer MATLAB work space demonstrate that the fuzzy controller associated to the genetic algorithms approach became very strong, gives a very good results and possesses good robustness.
Directory of Open Access Journals (Sweden)
Wei Huang
2013-01-01
Full Text Available We introduce a new category of fuzzy inference systems with the aid of a multiobjective opposition-based space search algorithm (MOSSA. The proposed MOSSA is essentially a multiobjective space search algorithm improved by using an opposition-based learning that employs a so-called opposite numbers mechanism to speed up the convergence of the optimization algorithm. In the identification of fuzzy inference system, the MOSSA is exploited to carry out the parametric identification of the fuzzy model as well as to realize its structural identification. Experimental results demonstrate the effectiveness of the proposed fuzzy models.
Gao, Xinbo; Li, Qi; Li, Jie
2003-09-01
Anchorperson shot detection is of significance for video shot semantic parsing and indexing clues extraction in content-based news video indexing and retrieval system. This paper presents a model-free anchorperson shot detection scheme based on the graph-theoretical clustering and fuzzy interference. First, a news video is segmented into video shots with any an effective video syntactic parsing algorithm. For each shot, one frame is extracted from the frame sequence as a representative key frame. Then the graph-theoretical clustering algorithm is performed on the key frames to identify the anchorperson frames. The anchorperson frames are further refined based on face detection and fuzzy interference with if-then rules. The proposed scheme achieves a precision of 98.40% and a recall of over 97.69% in the anchorperson shot detection experiment.
A hybrid intelligent algorithm for portfolio selection problem with fuzzy returns
Li, Xiang; Zhang, Yang; Wong, Hau-San; Qin, Zhongfeng
2009-11-01
Portfolio selection theory with fuzzy returns has been well developed and widely applied. Within the framework of credibility theory, several fuzzy portfolio selection models have been proposed such as mean-variance model, entropy optimization model, chance constrained programming model and so on. In order to solve these nonlinear optimization models, a hybrid intelligent algorithm is designed by integrating simulated annealing algorithm, neural network and fuzzy simulation techniques, where the neural network is used to approximate the expected value and variance for fuzzy returns and the fuzzy simulation is used to generate the training data for neural network. Since these models are used to be solved by genetic algorithm, some comparisons between the hybrid intelligent algorithm and genetic algorithm are given in terms of numerical examples, which imply that the hybrid intelligent algorithm is robust and more effective. In particular, it reduces the running time significantly for large size problems.
An object-oriented cluster search algorithm
Energy Technology Data Exchange (ETDEWEB)
Silin, Dmitry; Patzek, Tad
2003-01-24
In this work we describe two object-oriented cluster search algorithms, which can be applied to a network of an arbitrary structure. First algorithm calculates all connected clusters, whereas the second one finds a path with the minimal number of connections. We estimate the complexity of the algorithm and infer that the number of operations has linear growth with respect to the size of the network.
Directory of Open Access Journals (Sweden)
Yao-Tien Chen
2017-01-01
Full Text Available Segmentation of brain tissues is an important but inherently challenging task in that different brain tissues have similar grayscale values and the intensity of a brain tissue may be confused with that of another one. The paper accordingly develops an ICKFCM method based on kernelized fuzzy c-means clustering with ICA analysis for extracting regions of interest in MRI brain images. The proposed method first removes the skull region using a skull stripping algorithm. Through ICA, three independent components are then extracted from multimodal medical images containing T1-weighted, T2-weighted, and PD-weighted MRI images. As MRI signals can be regarded as a combination of the signals from brain matters, ICA can be used for contrast enhancement of MRI images. Finally, the three independent components are utilized as inputs by KFCM algorithm to extract different brain tissues. Relying on the decomposition of a multivariate signal into independent non-Gaussian components and using a more appropriate kernel-induced distance for fuzzy clustering, the proposed method is capable of achieving greater reliability in both theory and practice than other segmentation approaches. According to the experiment results, the proposed method is capable of accurately extracting the complicated shapes of brain tissues and still remaining robust against various types of noises.
An extended EM algorithm for subspace clustering
Institute of Scientific and Technical Information of China (English)
Lifei CHEN; Qingshan JIANG
2008-01-01
Clustering high dimensional data has become a challenge in data mining due to the curse of dimension-ality. To solve this problem, subspace clustering has been defined as an extension of traditional clustering that seeks to find clusters in subspaces spanned by different combinations of dimensions within a dataset. This paper presents a new subspace clustering algorithm that calcu-lates the local feature weights automatically in an EM-based clustering process. In the algorithm, the features are locally weighted by using a new unsupervised weight-ing method, as a means to minimize a proposed cluster-ing criterion that takes into account both the average intra-clusters compactness and the average inter-clusters separation for subspace clustering. For the purposes of capturing accurate subspace information, an additional outlier detection process is presented to identify the pos-sible local outliers of subspace clusters, and is embedded between the E-step and M-step of the algorithm. The method has been evaluated in clustering real-world gene expression data and high dimensional artificial data with outliers, and the experimental results have shown its effectiveness.
Universal triple I fuzzy reasoning algorithm of function model based on quotient space
Institute of Scientific and Technical Information of China (English)
Lu Qiang; Shen Guanting; and Liu Xiaoping
2012-01-01
Aiming at the deficiencies of analysis capacity from different levels and fuzzy treating method in product function modeling of conceptual design, the theory of quotient space and universal triple I fuzzy reasoning method are introduced, and then the function modeling algorithm based on the universal triple I fuzzy reasoning method is proposed. Firstly, the product function granular model based on the quotient space theory is built, with its function granular representation and computing rules defined at the same time. Secondly, in order to quickly achieve function granular model from function requirement, the function modeling method based on universal triple I fuzzy reasoning is put forward. Within the fuzzy reasoning of universal triple I method, the small-distance-activating method is proposed as the kernel of fuzzy reasoning; how to change function requirements to fuzzy ones, fuzzy computing methods, and strategy of fuzzy reasoning are respectively investigated as well; the function modeling algorithm based on the universal triple I fuzzy reasoning method is achieved. Lastly, the validity of the function granular model and function modeling algorithm is validated. Through our method, the reasonable function granular model can be quickly achieved from function requirements, and the fuzzy character of conceptual design can be well handled, which greatly improves conceptual design.
Julie, E. Golden; Selvi, S. Tamil
2016-01-01
Wireless sensor networks (WSNs) consist of sensor nodes with limited processing capability and limited nonrechargeable battery power. Energy consumption in WSN is a significant issue in networks for improving network lifetime. It is essential to develop an energy aware clustering protocol in WSN to reduce energy consumption for increasing network lifetime. In this paper, a neuro-fuzzy energy aware clustering scheme (NFEACS) is proposed to form optimum and energy aware clusters. NFEACS consists of two parts: fuzzy subsystem and neural network system that achieved energy efficiency in forming clusters and cluster heads in WSN. NFEACS used neural network that provides effective training set related to energy and received signal strength of all nodes to estimate the expected energy for tentative cluster heads. Sensor nodes with higher energy are trained with center location of base station to select energy aware cluster heads. Fuzzy rule is used in fuzzy logic part that inputs to form clusters. NFEACS is designed for WSN handling mobility of node. The proposed scheme NFEACS is compared with related clustering schemes, cluster-head election mechanism using fuzzy logic, and energy aware fuzzy unequal clustering. The experiment results show that NFEACS performs better than the other related schemes. PMID:26881269
Julie, E Golden; Selvi, S Tamil
2016-01-01
Wireless sensor networks (WSNs) consist of sensor nodes with limited processing capability and limited nonrechargeable battery power. Energy consumption in WSN is a significant issue in networks for improving network lifetime. It is essential to develop an energy aware clustering protocol in WSN to reduce energy consumption for increasing network lifetime. In this paper, a neuro-fuzzy energy aware clustering scheme (NFEACS) is proposed to form optimum and energy aware clusters. NFEACS consists of two parts: fuzzy subsystem and neural network system that achieved energy efficiency in forming clusters and cluster heads in WSN. NFEACS used neural network that provides effective training set related to energy and received signal strength of all nodes to estimate the expected energy for tentative cluster heads. Sensor nodes with higher energy are trained with center location of base station to select energy aware cluster heads. Fuzzy rule is used in fuzzy logic part that inputs to form clusters. NFEACS is designed for WSN handling mobility of node. The proposed scheme NFEACS is compared with related clustering schemes, cluster-head election mechanism using fuzzy logic, and energy aware fuzzy unequal clustering. The experiment results show that NFEACS performs better than the other related schemes.
Directory of Open Access Journals (Sweden)
E. Golden Julie
2016-01-01
Full Text Available Wireless sensor networks (WSNs consist of sensor nodes with limited processing capability and limited nonrechargeable battery power. Energy consumption in WSN is a significant issue in networks for improving network lifetime. It is essential to develop an energy aware clustering protocol in WSN to reduce energy consumption for increasing network lifetime. In this paper, a neuro-fuzzy energy aware clustering scheme (NFEACS is proposed to form optimum and energy aware clusters. NFEACS consists of two parts: fuzzy subsystem and neural network system that achieved energy efficiency in forming clusters and cluster heads in WSN. NFEACS used neural network that provides effective training set related to energy and received signal strength of all nodes to estimate the expected energy for tentative cluster heads. Sensor nodes with higher energy are trained with center location of base station to select energy aware cluster heads. Fuzzy rule is used in fuzzy logic part that inputs to form clusters. NFEACS is designed for WSN handling mobility of node. The proposed scheme NFEACS is compared with related clustering schemes, cluster-head election mechanism using fuzzy logic, and energy aware fuzzy unequal clustering. The experiment results show that NFEACS performs better than the other related schemes.
Tran, Huu-Khoa; Chiou, Juing -Shian; Peng, Shou-Tao
2016-01-01
In this paper, the feasibility of a Genetic Algorithm Optimization (GAO) education software based Fuzzy Logic Controller (GAO-FLC) for simulating the flight motion control of Unmanned Aerial Vehicles (UAVs) is designed. The generated flight trajectories integrate the optimized Scaling Factors (SF) fuzzy controller gains by using GAO algorithm. The…
Tran, Huu-Khoa; Chiou, Juing -Shian; Peng, Shou-Tao
2016-01-01
In this paper, the feasibility of a Genetic Algorithm Optimization (GAO) education software based Fuzzy Logic Controller (GAO-FLC) for simulating the flight motion control of Unmanned Aerial Vehicles (UAVs) is designed. The generated flight trajectories integrate the optimized Scaling Factors (SF) fuzzy controller gains by using GAO algorithm. The…
Zainuddin, Zarita; Lai, Kee Huong; Ong, Pauline
2013-04-01
Artificial neural networks (ANNs) are powerful mathematical models that are used to solve complex real world problems. Wavelet neural networks (WNNs), which were developed based on the wavelet theory, are a variant of ANNs. During the training phase of WNNs, several parameters need to be initialized; including the type of wavelet activation functions, translation vectors, and dilation parameter. The conventional k-means and fuzzy c-means clustering algorithms have been used to select the translation vectors. However, the solution vectors might get trapped at local minima. In this regard, the evolutionary harmony search algorithm, which is capable of searching for near-optimum solution vectors, both locally and globally, is introduced to circumvent this problem. In this paper, the conventional k-means and fuzzy c-means clustering algorithms were hybridized with the metaheuristic harmony search algorithm. In addition to obtaining the estimation of the global minima accurately, these hybridized algorithms also offer more than one solution to a particular problem, since many possible solution vectors can be generated and stored in the harmony memory. To validate the robustness of the proposed WNNs, the real world problem of epileptic seizure detection was presented. The overall classification accuracy from the simulation showed that the hybridized metaheuristic algorithms outperformed the standard k-means and fuzzy c-means clustering algorithms.
Data clustering theory, algorithms, and applications
Gan, Guojun; Wu, Jianhong
2007-01-01
Cluster analysis is an unsupervised process that divides a set of objects into homogeneous groups. This book starts with basic information on cluster analysis, including the classification of data and the corresponding similarity measures, followed by the presentation of over 50 clustering algorithms in groups according to some specific baseline methodologies such as hierarchical, center-based, and search-based methods. As a result, readers and users can easily identify an appropriate algorithm for their applications and compare novel ideas with existing results. The book also provides examples of clustering applications to illustrate the advantages and shortcomings of different clustering architectures and algorithms. Application areas include pattern recognition, artificial intelligence, information technology, image processing, biology, psychology, and marketing. Readers also learn how to perform cluster analysis with the C/C++ and MATLAB® programming languages.
Load Balancing Algorithm for Cache Cluster
Institute of Scientific and Technical Information of China (English)
刘美华; 古志民; 曹元大
2003-01-01
By the load definition of cluster, the request is regarded as granularity to compute load and implement the load balancing in cache cluster. First, the processing power of cache-node is studied from four aspects: network bandwidth, memory capacity, disk access rate and CPU usage. Then, the weighted load of cache-node is customized. Based on this, a load-balancing algorithm that can be applied to the cache cluster is proposed. Finally, Polygraph is used as a benchmarking tool to test the cache cluster possessing the load-balancing algorithm and the cache cluster with cache array routing protocol respectively. The results show the load-balancing algorithm can improve the performance of the cache cluster.
Semantic Based Cluster Content Discovery in Description First Clustering Algorithm
Directory of Open Access Journals (Sweden)
MUHAMMAD WASEEM KHAN
2017-01-01
Full Text Available In the field of data analytics grouping of like documents in textual data is a serious problem. A lot of work has been done in this field and many algorithms have purposed. One of them is a category of algorithms which firstly group the documents on the basis of similarity and then assign the meaningful labels to those groups. Description first clustering algorithm belong to the category in which the meaningful description is deduced first and then relevant documents are assigned to that description. LINGO (Label Induction Grouping Algorithm is the algorithm of description first clustering category which is used for the automatic grouping of documents obtained from search results. It uses LSI (Latent Semantic Indexing; an IR (Information Retrieval technique for induction of meaningful labels for clusters and VSM (Vector Space Model for cluster content discovery. In this paper we present the LINGO while it is using LSI during cluster label induction and cluster content discovery phase. Finally, we compare results obtained from the said algorithm while it uses VSM and Latent semantic analysis during cluster content discovery phase.
A Study on the Fuzzy-Logic-Based Solar Power MPPT Algorithms Using Different Fuzzy Input Variables
Directory of Open Access Journals (Sweden)
Jaw-Kuen Shiau
2015-04-01
Full Text Available Maximum power point tracking (MPPT is one of the key functions of the solar power management system in solar energy deployment. This paper investigates the design of fuzzy-logic-based solar power MPPT algorithms using different fuzzy input variables. Six fuzzy MPPT algorithms, based on different input variables, were considered in this study, namely (i slope (of solar power-versus-solar voltage and changes of the slope; (ii slope and variation of the power; (iii variation of power and variation of voltage; (iv variation of power and variation of current; (v sum of conductance and increment of the conductance; and (vi sum of angles of arctangent of the conductance and arctangent of increment of the conductance. Algorithms (i–(iv have two input variables each while algorithms (v and (vi use a single input variable. The fuzzy logic MPPT function is deployed using a buck-boost power converter. This paper presents the details of the determinations, considerations of the fuzzy rules, as well as advantages and disadvantages of each MPPT algorithm based upon photovoltaic (PV cell properties. The range of the input variable of Algorithm (vi is finite and the maximum power point condition is well defined in steady condition and, therefore, it can be used for multipurpose controller design. Computer simulations are conducted to verify the design.
Manikandan, P; Ramyachitra, D; Banupriya, D
2016-04-15
Proteins show their functional activity by interacting with other proteins and forms protein complexes since it is playing an important role in cellular organization and function. To understand the higher order protein organization, overlapping is an important step towards unveiling functional and evolutionary mechanisms behind biological networks. Most of the clustering algorithms do not consider the weighted as well as overlapping complexes. In this research, Prorank based Fuzzy algorithm has been proposed to find the overlapping protein complexes. The Fuzzy detection algorithm is incorporated in the Prorank algorithm after ranking step to find the overlapping community. The proposed algorithm executes in an iterative manner to compute the probability of robust clusters. The proposed and the existing algorithms were tested on different datasets such as PPI-D1, PPI-D2, Collins, DIP, Krogan Core and Krogan-Extended, gene expression such as GSE7645, GSE22269, GSE26923, pathways such as Meiosis, MAPK, Cell Cycle, phenotypes such as Yeast Heterogeneous and Yeast Homogeneous datasets. The experimental results show that the proposed algorithm predicts protein complexes with better accuracy compared to other state of art algorithms.
Fuzzy Preference Incorporated Evolutionary Algorithm for Multiobjective Optimization
Directory of Open Access Journals (Sweden)
Surafel Luleseged Tilahun
2011-01-01
Full Text Available Multiobjective evolutionary method is a way to overcome the limitation of the classical methods, by finding multiple solutions within a single run of the solution procedure. The aim of having a solution method for multiobjective optimization problem is to help the decision maker in getting the best solution. Usually the decision maker is not interested in a diverse set of Pareto optimal points. So, it is necessary to incorporate the decision maker’s preference so that the algorithm gives out alternative solutions around the decision maker’s preference. The problem in incorporating the decision maker’s preference is that the decision maker may not have a solid guide line in comparing tradeoffs of objectives. However, it is easy for the decision maker to compare in a fuzzy way. This paper discusses on incorporating a fuzzy tradeoffs in the evolutionary algorithm to zoom out the region where the decision maker’s preference lies. By using test functions it has shown that it is possible to give points in the region on the Pareto front where the decision maker’s interest lies.
Adaptive process control using fuzzy logic and genetic algorithms
Karr, C. L.
1993-01-01
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.
Parastar, Hadi; Bazrafshan, Alisina
2016-03-18
Fuzzy C-means clustering (FCM) is proposed as a promising method for the clustering of chromatographic fingerprints of complex samples, such as essential oils. As an example, secondary metabolites of 14 citrus leaves samples are extracted and analyzed by gas chromatography-mass spectrometry (GC-MS). The obtained chromatographic fingerprints are divided to desired number of chromatographic regions. Owing to the fact that chromatographic problems, such as elution time shift and peak overlap can significantly affect the clustering results, therefore, each chromatographic region is analyzed using multivariate curve resolution-alternating least squares (MCR-ALS) to address these problems. Then, the resolved elution profiles are used to make a new data matrix based on peak areas of pure components to cluster by FCM. The FCM clustering parameters (i.e., fuzziness coefficient and number of cluster) are optimized by two different methods of partial least squares (PLS) as a conventional method and minimization of FCM objective function as our new idea. The results showed that minimization of FCM objective function is an easier and better way to optimize FCM clustering parameters. Then, the optimized FCM clustering algorithm is used to cluster samples and variables to figure out the similarities and dissimilarities among samples and to find discriminant secondary metabolites in each cluster (chemotype). Finally, the FCM clustering results are compared with those of principal component analysis (PCA), hierarchical cluster analysis (HCA) and Kohonon maps. The results confirmed the outperformance of FCM over the frequently used clustering algorithms. Copyright © 2016 Elsevier B.V. All rights reserved.
Directory of Open Access Journals (Sweden)
Yuxian Zhang
2015-01-01
Full Text Available The quality index model in slashing process is difficult to build by reason of the outliers and noise data from original data. To the above problem, a fuzzy neural network based on non-Euclidean distance clustering is proposed in which the input space is partitioned into many local regions by the fuzzy clustering based on non-Euclidean distance so that the computation complexity is decreased, and fuzzy rule number is determined by validity function based on both the separation and the compactness among clusterings. Then, the premise parameters and consequent parameters are trained by hybrid learning algorithm. The parameters identification is realized; meanwhile the convergence condition of consequent parameters is obtained by Lyapunov function. Finally, the proposed method is applied to build the quality index model in slashing process in which the experimental data come from the actual slashing process. The experiment results show that the proposed fuzzy neural network for quality index model has lower computation complexity and faster convergence time, comparing with GP-FNN, BPNN, and RBFNN.
Performance comparison of fuzzy and non-fuzzy classification methods
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B. Simhachalam
2016-07-01
Full Text Available In data clustering, partition based clustering algorithms are widely used clustering algorithms. Among various partition algorithms, fuzzy algorithms, Fuzzy c-Means (FCM, Gustafson–Kessel (GK and non-fuzzy algorithm, k-means (KM are most popular methods. k-means and Fuzzy c-Means use standard Euclidian distance measure and Gustafson–Kessel uses fuzzy covariance matrix in their distance metrics. In this work, a comparative study of these algorithms with different famous real world data sets, liver disorder and wine from the UCI repository is presented. The performance of the three algorithms is analyzed based on the clustering output criteria. The results were compared with the results obtained from the repository. The results showed that Gustafson–Kessel produces close results to Fuzzy c-Means. Further, the experimental results demonstrate that k-means outperforms the Fuzzy c-Means and Gustafson–Kessel algorithms. Thus the efficiency of k-means is better than that of Fuzzy c-Means and Gustafson–Kessel algorithms.
A new algorithm of edge detection for color image: Generalized fuzzy operator
Institute of Scientific and Technical Information of China (English)
陈武凡; 鲁贤庆; 陈建军; 吴国雄
1995-01-01
The definition of the generalized fuzzy set is presented for the first time, and a generalized fuzzy operator is proposed to transform a generalized fuzzy set into a normal fuzzy set. The algorithm theory of the operator, as the newest method of the edge detection of a 2-D image, is successfully established. Many experiments haw proved that the algorithm is simpler, more rapid and more precise in location than other edge detection methods. And a schedule of the concrete performance has been given additionally about the edge detection of color images.
Remote triage support algorithm based on fuzzy logic.
Achkoski, Jugoslav; Koceski, S; Bogatinov, D; Temelkovski, B; Stevanovski, G; Kocev, I
2017-06-01
This paper presents a remote triage support algorithm as a part of a complex military telemedicine system which provides continuous monitoring of soldiers' vital sign data gathered on-site using unobtrusive set of sensors. The proposed fuzzy logic-based algorithm takes physiological data and classifies the casualties according to their health risk level, calculated following the Modified Early Warning Score (MEWS) methodology. To verify the algorithm, eight different evaluation scenarios using random vital sign data have been created. In each scenario, the hypothetical condition of the victims was assessed in parallel both by the system as well as by 50 doctors with significant experience in the field. The results showed that there is high (0.928) average correlation of the classification results. This suggests that the proposed algorithm can be used for automated remote triage in real life-saving situations even before the medical team arrives at the spot, and shorten the response times. Moreover, an additional study has been conducted in order to increase the computational efficiency of the algorithm, without compromising the quality of the classification results. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.
The Evaluation of Lane-Changing Behavior in Urban Traffic Stream with Fuzzy Clustering Method
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Ali Abdi
2012-11-01
Full Text Available We present a method for The Evaluation of Lane-Changing Behavior in Urban Traffic Stream with Fuzzy Clustering Method. The trends for drivers Lane-Changing with regard to remarkable effects in traffic are regarded as a major variable in traffic engineering. As a result, various algorithms have presented most models of Lane-Changing developed by means of lane information and the manner of vehicle movement mainly obtained from images process not much attention is given to the characteristics of driver. Lane change divided into two parts the first one are compulsory lane including lane change to turn left or turn right. The second type of change is optional and lane change to improve driving condition. A low speed car is a good example, in this study, through focused group discussion method, drivers information can be obtained so that driver’s personality traits are taken into consideration. Then drivers are divided into four groups by means of Algorithm clusters. The four Algorithms suggest that phase typed cluster is a more suitable method for drivers classification based on Lane-Changing. Through notarization of different type of scenarios of lane change in Iran following results released. The percentage of drivers for each group is 17/5, 35, 20 and 27/ %, respectively.
Non-convex polygons clustering algorithm
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Kruglikov Alexey
2016-01-01
Full Text Available A clustering algorithm is proposed, to be used as a preliminary step in motion planning. It is tightly coupled to the applied problem statement, i.e. uses parameters meaningful only with respect to it. Use of geometrical properties for polygons clustering allows for a better calculation time as opposed to general-purpose algorithms. A special form of map optimized for quick motion planning is constructed as a result.
The Georgi Algorithms of Jet Clustering
Ge, Shao-Feng
2014-01-01
We reveal the direct link between the jet clustering algorithms recently proposed by Howard Georgi and parton shower kinematics, providing firm foundation from the theoretical side. The kinematics of this class of elegant algorithms is explored systematically for partons with arbitrary masses and the jet function is generalized to $J^{(n)}_\\beta$ with a jet function index $n$ in order to achieve more degrees of freedom. Based on three basic requirements that, the result of jet clustering is p...
Directory of Open Access Journals (Sweden)
Bima Sena Bayu Dewantara
2014-12-01
Full Text Available Fuzzy rule optimization is a challenging step in the development of a fuzzy model. A simple two inputs fuzzy model may have thousands of combination of fuzzy rules when it deals with large number of input variations. Intuitively and trial‐error determination of fuzzy rule is very difficult. This paper addresses the problem of optimizing Fuzzy rule using Genetic Algorithm to compensate illumination effect in face recognition. Since uneven illumination contributes negative effects to the performance of face recognition, those effects must be compensated. We have developed a novel algorithmbased on a reflectance model to compensate the effect of illumination for human face recognition. We build a pair of model from a single image and reason those modelsusing Fuzzy.Fuzzy rule, then, is optimized using Genetic Algorithm. This approachspendsless computation cost by still keepinga high performance. Based on the experimental result, we can show that our algorithm is feasiblefor recognizing desired person under variable lighting conditions with faster computation time. Keywords: Face recognition, harsh illumination, reflectance model, fuzzy, genetic algorithm
Record Matching Over Query Results Using Fuzzy Ontological Document Clustering
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V.Vijayaraja
2011-02-01
Full Text Available Record matching is an essential step in duplicate detection as it identifies records representing same real-world entity. Supervised record matching methods require users to provide training data andtherefore cannot be applied for web databases where query results are generated on-the-fly. To overcome the problem, a new record matching method named Unsupervised Duplicate Elimination (UDE is proposed for identifying and eliminating duplicates among records in dynamic query results. The idea of this paper is to adjust the weights of record fields in calculating similarities among records. Two classifiers namely weight component similarity summing classifier, support vector machine classifier are iteratively employed with UDE where the first classifier utilizes the weights set to match records from different data sources. With the matched records as positive dataset and non duplicate records as negative set, the second classifier identifies new duplicates. Then, a new methodology to automatically interpret and cluster knowledge documents using an ontology schema is presented. Moreover, a fuzzy logic control approach is used to match suitable document cluster(s for given patents based on their derived ontological semantic webs. Thus, this paper takes advantage of similarity among records from web databases and solves the online duplicate detection problem.
FUZZY MULTI-LEVEL WAREHOUSE LAYOUT PROBLEM: NEW MODEL AND ALGORITHM
Institute of Scientific and Technical Information of China (English)
Lixing YANG; Yuan FENG
2006-01-01
This paper deals with a multi-level warehouse layout problem under fuzzy environment, in which different types of items need to be placed in a multi-level warehouse and the monthly demand of each item type and horizontal distance traveled by clamp track are treated as fuzzy variables. In order to minimize the total transportation cost, chance-constrained programming model is designed for the problem based on the credibility measure and then tabu search algorithm based on the fuzzy simulation is designed to solve the model. Some mathematical properties of the model are also discussed when the fuzzy variables are interval fuzzy numbers or trapezoidal fuzzy numbers. Finally, a numerical example is presented to show the efficiency of the algorithm.
Chen, Xin; Liu, Li; Zhou, Sida; Yue, Zhenjiang
2016-09-01
Reduced order models(ROMs) based on the snapshots on the CFD high-fidelity simulations have been paid great attention recently due to their capability of capturing the features of the complex geometries and flow configurations. To improve the efficiency and precision of the ROMs, it is indispensable to add extra sampling points to the initial snapshots, since the number of sampling points to achieve an adequately accurate ROM is generally unknown in prior, but a large number of initial sampling points reduces the parsimony of the ROMs. A fuzzy-clustering-based adding-point strategy is proposed and the fuzzy clustering acts an indicator of the region in which the precision of ROMs is relatively low. The proposed method is applied to construct the ROMs for the benchmark mathematical examples and a numerical example of hypersonic aerothermodynamics prediction for a typical control surface. The proposed method can achieve a 34.5% improvement on the efficiency than the estimated mean squared error prediction algorithm and shows same-level prediction accuracy.
clusterMaker: a multi-algorithm clustering plugin for Cytoscape
2011-01-01
Background In the post-genomic era, the rapid increase in high-throughput data calls for computational tools capable of integrating data of diverse types and facilitating recognition of biologically meaningful patterns within them. For example, protein-protein interaction data sets have been clustered to identify stable complexes, but scientists lack easily accessible tools to facilitate combined analyses of multiple data sets from different types of experiments. Here we present clusterMaker, a Cytoscape plugin that implements several clustering algorithms and provides network, dendrogram, and heat map views of the results. The Cytoscape network is linked to all of the other views, so that a selection in one is immediately reflected in the others. clusterMaker is the first Cytoscape plugin to implement such a wide variety of clustering algorithms and visualizations, including the only implementations of hierarchical clustering, dendrogram plus heat map visualization (tree view), k-means, k-medoid, SCPS, AutoSOME, and native (Java) MCL. Results Results are presented in the form of three scenarios of use: analysis of protein expression data using a recently published mouse interactome and a mouse microarray data set of nearly one hundred diverse cell/tissue types; the identification of protein complexes in the yeast Saccharomyces cerevisiae; and the cluster analysis of the vicinal oxygen chelate (VOC) enzyme superfamily. For scenario one, we explore functionally enriched mouse interactomes specific to particular cellular phenotypes and apply fuzzy clustering. For scenario two, we explore the prefoldin complex in detail using both physical and genetic interaction clusters. For scenario three, we explore the possible annotation of a protein as a methylmalonyl-CoA epimerase within the VOC superfamily. Cytoscape session files for all three scenarios are provided in the Additional Files section. Conclusions The Cytoscape plugin clusterMaker provides a number of clustering
clusterMaker: a multi-algorithm clustering plugin for Cytoscape
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Morris John H
2011-11-01
Full Text Available Abstract Background In the post-genomic era, the rapid increase in high-throughput data calls for computational tools capable of integrating data of diverse types and facilitating recognition of biologically meaningful patterns within them. For example, protein-protein interaction data sets have been clustered to identify stable complexes, but scientists lack easily accessible tools to facilitate combined analyses of multiple data sets from different types of experiments. Here we present clusterMaker, a Cytoscape plugin that implements several clustering algorithms and provides network, dendrogram, and heat map views of the results. The Cytoscape network is linked to all of the other views, so that a selection in one is immediately reflected in the others. clusterMaker is the first Cytoscape plugin to implement such a wide variety of clustering algorithms and visualizations, including the only implementations of hierarchical clustering, dendrogram plus heat map visualization (tree view, k-means, k-medoid, SCPS, AutoSOME, and native (Java MCL. Results Results are presented in the form of three scenarios of use: analysis of protein expression data using a recently published mouse interactome and a mouse microarray data set of nearly one hundred diverse cell/tissue types; the identification of protein complexes in the yeast Saccharomyces cerevisiae; and the cluster analysis of the vicinal oxygen chelate (VOC enzyme superfamily. For scenario one, we explore functionally enriched mouse interactomes specific to particular cellular phenotypes and apply fuzzy clustering. For scenario two, we explore the prefoldin complex in detail using both physical and genetic interaction clusters. For scenario three, we explore the possible annotation of a protein as a methylmalonyl-CoA epimerase within the VOC superfamily. Cytoscape session files for all three scenarios are provided in the Additional Files section. Conclusions The Cytoscape plugin cluster
Joint graph cut and relative fuzzy connectedness image segmentation algorithm.
Ciesielski, Krzysztof Chris; Miranda, Paulo A V; Falcão, Alexandre X; Udupa, Jayaram K
2013-12-01
We introduce an image segmentation algorithm, called GC(sum)(max), which combines, in novel manner, the strengths of two popular algorithms: Relative Fuzzy Connectedness (RFC) and (standard) Graph Cut (GC). We show, both theoretically and experimentally, that GC(sum)(max) preserves robustness of RFC with respect to the seed choice (thus, avoiding "shrinking problem" of GC), while keeping GC's stronger control over the problem of "leaking though poorly defined boundary segments." The analysis of GC(sum)(max) is greatly facilitated by our recent theoretical results that RFC can be described within the framework of Generalized GC (GGC) segmentation algorithms. In our implementation of GC(sum)(max) we use, as a subroutine, a version of RFC algorithm (based on Image Forest Transform) that runs (provably) in linear time with respect to the image size. This results in GC(sum)(max) running in a time close to linear. Experimental comparison of GC(sum)(max) to GC, an iterative version of RFC (IRFC), and power watershed (PW), based on a variety medical and non-medical images, indicates superior accuracy performance of GC(sum)(max) over these other methods, resulting in a rank ordering of GC(sum)(max)>PW∼IRFC>GC.
SPEQTACLE: An automated generalized fuzzy C-means algorithm for tumor delineation in PET
Energy Technology Data Exchange (ETDEWEB)
Lapuyade-Lahorgue, Jérôme; Visvikis, Dimitris; Hatt, Mathieu, E-mail: hatt@univ-brest.fr [LaTIM, INSERM, UMR 1101, Brest 29609 (France); Pradier, Olivier [LaTIM, INSERM, UMR 1101, Brest 29609, France and Radiotherapy Department, CHRU Morvan, Brest 29609 (France); Cheze Le Rest, Catherine [DACTIM University of Poitiers, Nuclear Medicine Department, CHU Milétrie, Poitiers 86021 (France)
2015-10-15
Purpose: Accurate tumor delineation in positron emission tomography (PET) images is crucial in oncology. Although recent methods achieved good results, there is still room for improvement regarding tumors with complex shapes, low signal-to-noise ratio, and high levels of uptake heterogeneity. Methods: The authors developed and evaluated an original clustering-based method called spatial positron emission quantification of tumor—Automatic Lp-norm estimation (SPEQTACLE), based on the fuzzy C-means (FCM) algorithm with a generalization exploiting a Hilbertian norm to more accurately account for the fuzzy and non-Gaussian distributions of PET images. An automatic and reproducible estimation scheme of the norm on an image-by-image basis was developed. Robustness was assessed by studying the consistency of results obtained on multiple acquisitions of the NEMA phantom on three different scanners with varying acquisition parameters. Accuracy was evaluated using classification errors (CEs) on simulated and clinical images. SPEQTACLE was compared to another FCM implementation, fuzzy local information C-means (FLICM) and fuzzy locally adaptive Bayesian (FLAB). Results: SPEQTACLE demonstrated a level of robustness similar to FLAB (variability of 14% ± 9% vs 14% ± 7%, p = 0.15) and higher than FLICM (45% ± 18%, p < 0.0001), and improved accuracy with lower CE (14% ± 11%) over both FLICM (29% ± 29%) and FLAB (22% ± 20%) on simulated images. Improvement was significant for the more challenging cases with CE of 17% ± 11% for SPEQTACLE vs 28% ± 22% for FLAB (p = 0.009) and 40% ± 35% for FLICM (p < 0.0001). For the clinical cases, SPEQTACLE outperformed FLAB and FLICM (15% ± 6% vs 37% ± 14% and 30% ± 17%, p < 0.004). Conclusions: SPEQTACLE benefitted from the fully automatic estimation of the norm on a case-by-case basis. This promising approach will be extended to multimodal images and multiclass estimation in future developments.
Novel density-based and hierarchical density-based clustering algorithms for uncertain data.
Zhang, Xianchao; Liu, Han; Zhang, Xiaotong
2017-09-01
Uncertain data has posed a great challenge to traditional clustering algorithms. Recently, several algorithms have been proposed for clustering uncertain data, and among them density-based techniques seem promising for handling data uncertainty. However, some issues like losing uncertain information, high time complexity and nonadaptive threshold have not been addressed well in the previous density-based algorithm FDBSCAN and hierarchical density-based algorithm FOPTICS. In this paper, we firstly propose a novel density-based algorithm PDBSCAN, which improves the previous FDBSCAN from the following aspects: (1) it employs a more accurate method to compute the probability that the distance between two uncertain objects is less than or equal to a boundary value, instead of the sampling-based method in FDBSCAN; (2) it introduces new definitions of probability neighborhood, support degree, core object probability, direct reachability probability, thus reducing the complexity and solving the issue of nonadaptive threshold (for core object judgement) in FDBSCAN. Then, we modify the algorithm PDBSCAN to an improved version (PDBSCANi), by using a better cluster assignment strategy to ensure that every object will be assigned to the most appropriate cluster, thus solving the issue of nonadaptive threshold (for direct density reachability judgement) in FDBSCAN. Furthermore, as PDBSCAN and PDBSCANi have difficulties for clustering uncertain data with non-uniform cluster density, we propose a novel hierarchical density-based algorithm POPTICS by extending the definitions of PDBSCAN, adding new definitions of fuzzy core distance and fuzzy reachability distance, and employing a new clustering framework. POPTICS can reveal the cluster structures of the datasets with different local densities in different regions better than PDBSCAN and PDBSCANi, and it addresses the issues in FOPTICS. Experimental results demonstrate the superiority of our proposed algorithms over the existing
A Generalized Automatic Hybrid Fuzzy-Based GA-PSO Clustering Approach
Directory of Open Access Journals (Sweden)
Amir Hooshang Mazinan, ,
2014-09-01
Full Text Available The main contribution of the present research arises from developing the traditional methods in the area of segmentation of brain magnetic resonance imaging (MRI. Contemporary research is now developing techniques to solve the whole considerable problems in this field, such as the fuzzy local information c-mean (FLICM approach that incorporate the local spatial and the gray level information. It should be noted that the present approach is robust against noise, although the high computational complexity is not truly ignored. A novel approach in segmentation of brain MRI has been investigated and presented through the proposed research. Because of so many noises embedded in the acquiring procedure, like eddy currents, the segmentation of the brain MR is now tangibly taken into account as a difficult task. Fuzzy-based clustering algorithm is one of the solutions in the same way. But, it is so sensitive to change through noise and other imaging artifacts. The idea of combining the genetic algorithm (GA and particle swarm optimization (PSO for the purpose of generalizing the FLICM is the ultimate goal in the present investigation, since the computational complexity could actually be reduced. The experiments with a number of simulated images as well as the clinical MRI data illustrate that the proposed approach is applicable and effective.
Optimal Hops-Based Adaptive Clustering Algorithm
Xuan, Xin; Chen, Jian; Zhen, Shanshan; Kuo, Yonghong
This paper proposes an optimal hops-based adaptive clustering algorithm (OHACA). The algorithm sets an energy selection threshold before the cluster forms so that the nodes with less energy are more likely to go to sleep immediately. In setup phase, OHACA introduces an adaptive mechanism to adjust cluster head and load balance. And the optimal distance theory is applied to discover the practical optimal routing path to minimize the total energy for transmission. Simulation results show that OHACA prolongs the life of network, improves utilizing rate and transmits more data because of energy balance.
Issues Challenges and Tools of Clustering Algorithms
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Parul Agarwal
2011-05-01
Full Text Available Clustering is an unsupervised technique of Data Mining. It means grouping similar objects together and separating the dissimilar ones. Each object in the data set is assigned a class label in the clustering process using a distance measure. This paper has captured the problems that are faced in real when clustering algorithms are implemented .It also considers the most extensively used tools which are readily available and support functions which ease the programming. Once algorithms have been implemented, they also need to be tested for its validity. There exist several validation indexes for testing the performance and accuracy which have also been discussed here.
Blockspin Cluster Algorithms for Quantum Spin Systems
Wiese, U J
1992-01-01
Cluster algorithms are developed for simulating quantum spin systems like the one- and two-dimensional Heisenberg ferro- and anti-ferromagnets. The corresponding two- and three-dimensional classical spin models with four-spin couplings are maped to blockspin models with two-blockspin interactions. Clusters of blockspins are updated collectively. The efficiency of the method is investigated in detail for one-dimensional spin chains. Then in most cases the new algorithms solve the problems of slowing down from which standard algorithms are suffering.
Access Network Selection Based on Fuzzy Logic and Genetic Algorithms
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Mohammed Alkhawlani
2008-01-01
Full Text Available In the next generation of heterogeneous wireless networks (HWNs, a large number of different radio access technologies (RATs will be integrated into a common network. In this type of networks, selecting the most optimal and promising access network (AN is an important consideration for overall networks stability, resource utilization, user satisfaction, and quality of service (QoS provisioning. This paper proposes a general scheme to solve the access network selection (ANS problem in the HWN. The proposed scheme has been used to present and design a general multicriteria software assistant (SA that can consider the user, operator, and/or the QoS view points. Combined fuzzy logic (FL and genetic algorithms (GAs have been used to give the proposed scheme the required scalability, flexibility, and simplicity. The simulation results show that the proposed scheme and SA have better and more robust performance over the random-based selection.
Optimization of type-2 fuzzy controllers using the bee colony algorithm
Amador, Leticia
2017-01-01
This book focuses on the fields of fuzzy logic, bio-inspired algorithm; especially bee colony optimization algorithm and also considering the fuzzy control area. The main idea is that this areas together can to solve various control problems and to find better results. In this book we test the proposed method using two benchmark problems; the problem for filling a water tank and the problem for controlling the trajectory in an autonomous mobile robot. When Interval Type-2 Fuzzy Logic System is implemented to model the behavior of systems, the results show a better stabilization, because the analysis of uncertainty is better. For this reason we consider in this book the proposed method using fuzzy systems, fuzzy controllers, and bee colony optimization algorithm improve the behavior of the complex control problems.
A New Clustering Algorithm for Face Classification
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Shaker K. Ali
2016-06-01
Full Text Available In This paper, we proposed new clustering algorithm depend on other clustering algorithm ideas. The proposed algorithm idea is based on getting distance matrix, then the exclusion of the matrix points which will be clustered by saving the location (row, column of these points and determine the minimum distance of these points which will be belongs the group (class and keep the other points which are not clustering yet. The propose algorithm is applied to image data base of the human face with different environment (direction, angles... etc.. These data are collected from different resource (ORL site and real images collected from random sample of Thi_Qar city population in lraq. Our algorithm has been implemented on three types of distance to calculate the minimum distance between points (Euclidean, Correlation and Minkowski distance .The efficiency ratio of proposed algorithm has varied according to the data base and threshold, the efficiency of our algorithm is exceeded (96%. Matlab (2014 has been used in this work.
Self-learning Fuzzy Controllers Based On a Real-time Reinforcement Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
FANG Jian-an; MIAO Qing-ying; GUO Zhao-xia; SHAO Shi-huang
2002-01-01
This paper presents a novel method for constructing fuzzy controllers based on a real time reinforcement genetic algorithm. This methodology introduces the real-time learning capability of neural networks into globally searching process of genetic algorithm, aiming to enhance the convergence rate and real-time learning ability of genetic algorithm, which is then used to construct fuzzy controllers for complex dynamic systems without any knowledge about system dynamics and prior control experience. The cart-pole system is employed as a test bed to demonstrate the effectiveness of the proposed control scheme, and the robustness of the acquired fuzzy controller with comparable result.
RSPOP: rough set-based pseudo outer-product fuzzy rule identification algorithm.
Ang, Kai Keng; Quek, Chai
2005-01-01
System modeling with neuro-fuzzy systems involves two contradictory requirements: interpretability verses accuracy. The pseudo outer-product (POP) rule identification algorithm used in the family of pseudo outer-product-based fuzzy neural networks (POPFNN) suffered from an exponential increase in the number of identified fuzzy rules and computational complexity arising from high-dimensional data. This decreases the interpretability of the POPFNN in linguistic fuzzy modeling. This article proposes a novel rough set-based pseudo outer-product (RSPOP) algorithm that integrates the sound concept of knowledge reduction from rough set theory with the POP algorithm. The proposed algorithm not only performs feature selection through the reduction of attributes but also extends the reduction to rules without redundant attributes. As many possible reducts exist in a given rule set, an objective measure is developed for POPFNN to correctly identify the reducts that improve the inferred consequence. Experimental results are presented using published data sets and real-world application involving highway traffic flow prediction to evaluate the effectiveness of using the proposed algorithm to identify fuzzy rules in the POPFNN using compositional rule of inference and singleton fuzzifier (POPFNN-CRI(S)) architecture. Results showed that the proposed rough set-based pseudo outer-product algorithm reduces computational complexity, improves the interpretability of neuro-fuzzy systems by identifying significantly fewer fuzzy rules, and improves the accuracy of the POPFNN.
A Survey of Grid Based Clustering Algorithms
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MR ILANGO
2010-08-01
Full Text Available Cluster Analysis, an automatic process to find similar objects from a database, is a fundamental operation in data mining. A cluster is a collection of data objects that are similar to one another within the same cluster and are dissimilar to the objects in other clusters. Clustering techniques have been discussed extensively in SimilaritySearch, Segmentation, Statistics, Machine Learning, Trend Analysis, Pattern Recognition and Classification [1]. Clustering methods can be classified into i Partitioning methods ii Hierarchical methods iii Density-based methods iv Grid-based methods v Model-based methods. Grid based methods quantize the object space into a finite number of cells (hyper-rectangles and then perform the required operations on the quantized space. The main advantage of Grid based method is its fast processing time which depends on number of cells in each dimension in quantized space. In this research paper, we present some of the grid based methods such as CLIQUE (CLustering In QUEst [2], STING (STatistical INformation Grid [3], MAFIA (Merging of Adaptive Intervals Approach to Spatial Data Mining [4], Wave Cluster [5]and O-CLUSTER (Orthogonal partitioning CLUSTERing [6], as a survey andalso compare their effectiveness in clustering data objects. We also present some of the latest developments in Grid Based methods such as Axis Shifted Grid Clustering Algorithm [7] and Adaptive Mesh Refinement [Wei-Keng Liao etc] [8] to improve the processing time of objects.
Fuzzy forecasting based on fuzzy-trend logical relationship groups.
Chen, Shyi-Ming; Wang, Nai-Yi
2010-10-01
In this paper, we present a new method to predict the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy-trend logical relationship groups (FTLRGs). The proposed method divides fuzzy logical relationships into FTLRGs based on the trend of adjacent fuzzy sets appearing in the antecedents of fuzzy logical relationships. First, we apply an automatic clustering algorithm to cluster the historical data into intervals of different lengths. Then, we define fuzzy sets based on these intervals of different lengths. Then, the historical data are fuzzified into fuzzy sets to derive fuzzy logical relationships. Then, we divide the fuzzy logical relationships into FTLRGs for forecasting the TAIEX. Moreover, we also apply the proposed method to forecast the enrollments and the inventory demand, respectively. The experimental results show that the proposed method gets higher average forecasting accuracy rates than the existing methods.
Cluster hybrid Monte Carlo simulation algorithms
Plascak, J. A.; Ferrenberg, Alan M.; Landau, D. P.
2002-06-01
We show that addition of Metropolis single spin flips to the Wolff cluster-flipping Monte Carlo procedure leads to a dramatic increase in performance for the spin-1/2 Ising model. We also show that adding Wolff cluster flipping to the Metropolis or heat bath algorithms in systems where just cluster flipping is not immediately obvious (such as the spin-3/2 Ising model) can substantially reduce the statistical errors of the simulations. A further advantage of these methods is that systematic errors introduced by the use of imperfect random-number generation may be largely healed by hybridizing single spin flips with cluster flipping.
DEFF Research Database (Denmark)
Khoobi, Saeed; Halvaei, Abolfazl; Hajizadeh, Amin
2016-01-01
of EV relies too much on the expert experience and it may lead to sub-optimal performance. This paper develops an optimized fuzzy controller using genetic algorithm (GA) for an electric vehicle equipped with two power bank including battery and super-capacitor. The model of EV and optimized fuzzy...
control of a dc motor using fuzzy logic control algorithm
African Journals Online (AJOL)
user
conditions such as changes in motor load demand, non- linearity ... Figure 1: Structure of a fuzzy logic controller (Source. [6]). A typical fuzzy logic ... mathematical modeling based on first principles; and via ..... applied. On the premise of these findings, it would be tactful in ... and Sugeno Type Fuzzy Inference Systems for Air.
The Application of Imperialist Competitive Algorithm for Fuzzy Random Portfolio Selection Problem
EhsanHesamSadati, Mir; Bagherzadeh Mohasefi, Jamshid
2013-10-01
This paper presents an implementation of the Imperialist Competitive Algorithm (ICA) for solving the fuzzy random portfolio selection problem where the asset returns are represented by fuzzy random variables. Portfolio Optimization is an important research field in modern finance. By using the necessity-based model, fuzzy random variables reformulate to the linear programming and ICA will be designed to find the optimum solution. To show the efficiency of the proposed method, a numerical example illustrates the whole idea on implementation of ICA for fuzzy random portfolio selection problem.
A novel compensation-based recurrent fuzzy neural network and its learning algorithm
Institute of Scientific and Technical Information of China (English)
WU Bo; WU Ke; LU JianHong
2009-01-01
Based on detailed atudy on aeveral kinds of fuzzy neural networks, we propose a novel compensation. based recurrent fuzzy neural network (CRFNN) by adding recurrent element and compensatory element to the conventional fuzzy neural network. Then, we propose a sequential learning method for the structure Identification of the CRFNN In order to confirm the fuzzy rules and their correlaUve parameters effectively. Furthermore, we Improve the BP algorithm based on the characteristics of the proposed CRFNN to train the network. By modeling the typical nonlinear systems, we draw the conclusion that the proposed CRFNN has excellent dynamic response and strong learning ability.
Polyclonal clustering algorithm and its convergence
Institute of Scientific and Technical Information of China (English)
MA Li; JIAO Li-cheng; BAI Lin; CHEN Chang-guo
2008-01-01
Being characteristic of non-teacher learning, self-organization, memory, and noise resistance, the artificial immune system is a research focus in the field of intelligent information processing. Based on the basic principles of organism immune and clonal selection, this article presents a polyclonal clustering algorithm characteristic of self-adaptation. According to the core idea of the algorithm, various immune operators in the artificial immune system are employed in the clustering process; moreover, clustering numbers are adjusted in accordance with the affinity function. Introduction of the recombination operator can effectively enhance the diversity of the individual antibody in a generation population, so that the searching scope for solutions is enlarged and the premature phenomenon of the algorithm is avoided. Besides, introduction of the inconsistent mutation operator enhances the adaptability and optimizes the performance of local solution seeking. Meanwhile, the convergence of the algorithm is accelerated. In addition, the article also proves the convergence of the algorithm by employing the Markov chain. Results of the data simulation experiment show that the algorithm is capable of obtaining reasonable and effective cluster.
Lucieer, A.; Veen, L.E.
2009-01-01
Uncertainty and vagueness are important concepts when dealing with transition zones between vegetation communities or land-cover classes. In this study, classification uncertainty is quantified by applying a supervised fuzzy classification algorithm. New visualization techniques are proposed and pre
Maximum-entropy clustering algorithm and its global convergence analysis
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Constructing a batch of differentiable entropy functions touniformly approximate an objective function by means of the maximum-entropy principle, a new clustering algorithm, called maximum-entropy clustering algorithm, is proposed based on optimization theory. This algorithm is a soft generalization of the hard C-means algorithm and possesses global convergence. Its relations with other clustering algorithms are discussed.
A Robust Background Removal Algortihms Using Fuzzy C-Means Clustering
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S.Lakshmi
2013-04-01
Full Text Available Background subtraction is typically one of the first steps carried out in motion detection using static video cameras. This paper presents a novel method for background removal that processes only some pixels of each image. Some regions of interest of the objects in the image or frame are located with the help of edgedetector. Once the region is detected only that area will be segmented instead of processing the whole image. This method achieves a significant reduction in computation time that can be used forsubsequent image analysis. In this paper we detect the foreground object with the help of edge detector and combinethe Fuzzy c-means clustering algorithm to segment the object by means of subtracting the current frame from the previous frame, the accuratebackground is identified.
Analytical Model and Algorithm of Fuzzy Fault Tree
Institute of Scientific and Technical Information of China (English)
杨艺; 何学秋; 王恩元; 刘贞堂
2002-01-01
In the past, the probabilities of basic events were described as triangular or trapezoidal fuzzy number that cannot characterize the common distribution of the primary events in engineering, and the fault tree analyzed by fuzzy set theory did not include repeated basic events. This paper presents a new method to a nalyze the fault tree by using normal fuzzy number to describe the fuzzy probability of each basic event which is more suitably used to analyze the reliability in safety systems, and then the formulae of computing the fuzzy probability of the top event of the fault tree which includes repeated events are derived. Finally, an example is given.
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Burhan Ergen
2014-01-01
Full Text Available This paper proposes two edge detection methods for medical images by integrating the advantages of Gabor wavelet transform (GWT and unsupervised clustering algorithms. The GWT is used to enhance the edge information in an image while suppressing noise. Following this, the k-means and Fuzzy c-means (FCM clustering algorithms are used to convert a gray level image into a binary image. The proposed methods are tested using medical images obtained through Computed Tomography (CT and Magnetic Resonance Imaging (MRI devices, and a phantom image. The results prove that the proposed methods are successful for edge detection, even in noisy cases.
Ergen, Burhan
2014-01-01
This paper proposes two edge detection methods for medical images by integrating the advantages of Gabor wavelet transform (GWT) and unsupervised clustering algorithms. The GWT is used to enhance the edge information in an image while suppressing noise. Following this, the k-means and Fuzzy c-means (FCM) clustering algorithms are used to convert a gray level image into a binary image. The proposed methods are tested using medical images obtained through Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) devices, and a phantom image. The results prove that the proposed methods are successful for edge detection, even in noisy cases.
Fuzzy 2-partition entropy threshold selection based on Big Bang–Big Crunch Optimization algorithm
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Baljit Singh Khehra
2015-03-01
Full Text Available The fuzzy 2-partition entropy approach has been widely used to select threshold value for image segmenting. This approach used two parameterized fuzzy membership functions to form a fuzzy 2-partition of the image. The optimal threshold is selected by searching an optimal combination of parameters of the membership functions such that the entropy of fuzzy 2-partition is maximized. In this paper, a new fuzzy 2-partition entropy thresholding approach based on the technology of the Big Bang–Big Crunch Optimization (BBBCO is proposed. The new proposed thresholding approach is called the BBBCO-based fuzzy 2-partition entropy thresholding algorithm. BBBCO is used to search an optimal combination of parameters of the membership functions for maximizing the entropy of fuzzy 2-partition. BBBCO is inspired by the theory of the evolution of the universe; namely the Big Bang and Big Crunch Theory. The proposed algorithm is tested on a number of standard test images. For comparison, three different algorithms included Genetic Algorithm (GA-based, Biogeography-based Optimization (BBO-based and recursive approaches are also implemented. From experimental results, it is observed that the performance of the proposed algorithm is more effective than GA-based, BBO-based and recursion-based approaches.
A Development of Self-Organization Algorithm for Fuzzy Logic Controller
Energy Technology Data Exchange (ETDEWEB)
Park, Y.M.; Moon, U.C. [Seoul National Univ. (Korea, Republic of). Coll. of Engineering; Lee, K.Y. [Pennsylvania State Univ., University Park, PA (United States). Dept. of Electrical Engineering
1994-09-01
This paper proposes a complete design method for an on-line self-organizing fuzzy logic controller without using any plant model. By mimicking the human learning process, the control algorithm finds control rules of a system for which little knowledge has been known. To realize this, a concept of Fuzzy Auto-Regressive Moving Average(FARMA) rule is introduced. In a conventional fuzzy logic control, knowledge on the system supplied by an expert is required in developing control rules. However, the proposed new fuzzy logic controller needs no expert in making control rules. Instead, rules are generated using the history of input-output pairs, and new inference and defuzzification methods are developed. The generated rules are strode in the fuzzy rule space and updated on-line by a self-organizing procedure. The validity of the proposed fuzzy logic control method has been demonstrated numerically in controlling an inverted pendulum. (author). 28 refs., 16 figs.
An Adaptive Clustering Algorithm for Intrusion Detection
Institute of Scientific and Technical Information of China (English)
QIU Juli
2007-01-01
In this paper,we introduce an adaptive clustering algorithm for intrusion detection based on wavecluster which was introduced by Gholamhosein in 1999 and used with success in image processing.Because of the non-stationary characteristic of network traffic,we extend and develop an adaptive wavecluster algorithm for intrusion detection.Using the multiresolution property of wavelet transforms,we can effectively identify arbitrarily shaped clusters at different scales and degrees of detail,moreover,applying wavelet transform removes the noise from the original feature space and make more accurate cluster found.Experimental results on KDD-99 intrusion detection dataset show the efficiency and accuracy of this algorithm.A detection rate above 96% and a false alarm rate below 3% are achieved.
Fuzzy clustering of EEE components for space industry
Orlov, V. I.; Stashkov, D. V.; Kazakovtsev, L. A.; Stupina, A. A.
2016-11-01
One of the most important problems of the space industry is obtaining reliable methods of automatic grouping (clustering) of specialized EEE components for using in space systems. The main purpose of automatic grouping of EEE components on a set different parameters is the most legible splitting group of EEE components into several homogeneous production batches produced from a single bath of raw materials. The Expectation Maximization algorithm first time applied for the classification of EEE components.
Hybrid fuzzy charged system search algorithm based state estimation in distribution networks
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Sachidananda Prasad
2017-06-01
Full Text Available This paper proposes a new hybrid charged system search (CSS algorithm based state estimation in radial distribution networks in fuzzy framework. The objective of the optimization problem is to minimize the weighted square of the difference between the measured and the estimated quantity. The proposed method of state estimation considers bus voltage magnitude and phase angle as state variable along with some equality and inequality constraints for state estimation in distribution networks. A rule based fuzzy inference system has been designed to control the parameters of the CSS algorithm to achieve better balance between the exploration and exploitation capability of the algorithm. The efficiency of the proposed fuzzy adaptive charged system search (FACSS algorithm has been tested on standard IEEE 33-bus system and Indian 85-bus practical radial distribution system. The obtained results have been compared with the conventional CSS algorithm, weighted least square (WLS algorithm and particle swarm optimization (PSO for feasibility of the algorithm.
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Xian-xia Zhang
2012-01-01
Full Text Available Many industrial processes and physical systems are spatially distributed systems. Recently, a novel 3-D FLC was developed for such systems. The previous study on the 3-D FLC was concentrated on an expert knowledge-based approach. However, in most of situations, we may lack the expert knowledge, while input-output data sets hidden with effective control laws are usually available. Under such circumstance, a data-driven approach could be a very effective way to design the 3-D FLC. In this study, we aim at developing a new 3-D FLC design methodology based on clustering and support vector machine (SVM regression. The design consists of three parts: initial rule generation, rule-base simplification, and parameter learning. Firstly, the initial rules are extracted by a nearest neighborhood clustering algorithm with Frobenius norm as a distance. Secondly, the initial rule-base is simplified by merging similar 3-D fuzzy sets and similar 3-D fuzzy rules based on similarity measure technique. Thirdly, the consequent parameters are learned by a linear SVM regression algorithm. Additionally, the universal approximation capability of the proposed 3-D fuzzy system is discussed. Finally, the control of a catalytic packed-bed reactor is taken as an application to demonstrate the effectiveness of the proposed 3-D FLC design.
Efficient Cluster Head Selection Algorithm for MANET
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Khalid Hussain
2013-01-01
Full Text Available In mobile ad hoc network (MANET cluster head selection is considered a gigantic challenge. In wireless sensor network LEACH protocol can be used to select cluster head on the bases of energy, but it is still a dispute in mobil ad hoc networks and especially when nodes are itinerant. In this paper we proposed an efficient cluster head selection algorithm (ECHSA, for selection of the cluster head efficiently in Mobile ad hoc networks. We evaluate our proposed algorithm through simulation in OMNet++ as well as on test bed; we experience the result according to our assumption. For further evaluation we also compare our proposed protocol with several other protocols like LEACH-C and consequences show perfection.
Performance Analysis of Hierarchical Clustering Algorithm
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K.Ranjini
2011-07-01
Full Text Available Clustering is the classification of objects into different groups, or more precisely, the partitioning of a data set into subsets (clusters, so that the data in each subset (ideally share some common trait - often proximity according to some defined distance measure. Data clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. This paper explains the implementation of agglomerative and divisive clustering algorithms applied on various types of data. The details of the victims of Tsunami in Thailand during the year 2004, was taken as the test data. Visual programming is used for implementation and running time of the algorithms using different linkages (agglomerative to different types of data are taken for analysis.
The Ways of Fuzzy Control Algorithms Using for Harvesting Machines Tracking
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L. Tóth
2013-09-01
Full Text Available This contribution is oriented to ways of a fuzzy regulation using for machine tracking of the harvest machines. The main aim of this work was to practice verify and evaluate of functionality of control fuzzy algorithms for an Ackerman’s chassis which are generally used in agriculture machines for the crops harvesting. Design of the fuzzy control algorithm was focused to the wall following algorithm and obstacle avoidance. To achieve of the reliable results was made the real model of vehicle with Ackerman’s chassis type, which was controlled by PC with using development board Stellaris LM3S8962 based on ARM processor. Fuzzy control algorithms were developed in LabView application. Deviations were up to 0.2 m, which can be reduced to 0.1 m by hardware changing.
A FUZZY-LOGIC CONTROL ALGORITHM FOR ACTIVE QUEUE MANAGEMENT IN IP NETWORKS
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
Active Queue Management (AQM) is an active research area in the Internet community. Random Early Detection (RED) is a typical AQM algorithm, but it is known that it is difficult to configure its parameters and its average queue length is closely related to the load level. This paper proposes an effective fuzzy congestion control algorithm based on fuzzy logic which uses the predominance of fuzzy logic to deal with uncertain events. The main advantage of this new congestion control algorithm is that it discards the packet dropping mechanism of RED, and calculates packet loss according to a preconfigured fuzzy logic by using the queue length and the buffer usage ratio. Theoretical analysis and Network Simulator (NS) simulation results show that the proposed algorithm achieves more throughput and more stable queue length than traditional schemes. It really improves a router's ability in network congestion control in IP network.
Implementation of spectral clustering on microarray data of carcinoma using k-means algorithm
Frisca, Bustamam, Alhadi; Siswantining, Titin
2017-03-01
Clustering is one of data analysis methods that aims to classify data which have similar characteristics in the same group. Spectral clustering is one of the most popular modern clustering algorithms. As an effective clustering technique, spectral clustering method emerged from the concepts of spectral graph theory. Spectral clustering method needs partitioning algorithm. There are some partitioning methods including PAM, SOM, Fuzzy c-means, and k-means. Based on the research that has been done by Capital and Choudhury in 2013, when using Euclidian distance k-means algorithm provide better accuracy than PAM algorithm. So in this paper we use k-means as our partition algorithm. The major advantage of spectral clustering is in reducing data dimension, especially in this case to reduce the dimension of large microarray dataset. Microarray data is a small-sized chip made of a glass plate containing thousands and even tens of thousands kinds of genes in the DNA fragments derived from doubling cDNA. Application of microarray data is widely used to detect cancer, for the example is carcinoma, in which cancer cells express the abnormalities in his genes. The purpose of this research is to classify the data that have high similarity in the same group and the data that have low similarity in the others. In this research, Carcinoma microarray data using 7457 genes. The result of partitioning using k-means algorithm is two clusters.
A Self-tuning Fuzzy Queue Management Algorithm for Congestion Control
Institute of Scientific and Technical Information of China (English)
Zhang Jingyuan(张敬辕); Xie Jianying
2004-01-01
This letter presents an effective self-tuning fuzzy queue management algorithm for congestion control. With the application of the algorithm, routers in IP network regulate its packet drop probability by a self-tuning fuzzy controller. The main advantage of the algorithm is that, with the parameter self-tuning mechanism, queue length can keep stable in a variety of network environments without the difficulty of parameter configuration. Simulations show that the algorithm is efficient, stable and outperforms the popular RED queue management algorithm significantly.
A hybrid model for bankruptcy prediction using genetic algorithm, fuzzy c-means and mars
Martin, A; Saranya, G; Gayathri, P; Venkatesan, Prasanna
2011-01-01
Bankruptcy prediction is very important for all the organization since it affects the economy and rise many social problems with high costs. There are large number of techniques have been developed to predict the bankruptcy, which helps the decision makers such as investors and financial analysts. One of the bankruptcy prediction models is the hybrid model using Fuzzy C-means clustering and MARS, which uses static ratios taken from the bank financial statements for prediction, which has its own theoretical advantages. The performance of existing bankruptcy model can be improved by selecting the best features dynamically depend on the nature of the firm. This dynamic selection can be accomplished by Genetic Algorithm and it improves the performance of prediction model.
A HYBRID MODEL FOR BANKRUPTCY PREDICTION USING GENETIC ALGORITHM, FUZZY C-MEANS AND MARS
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A.Martin
2011-05-01
Full Text Available Bankruptcy prediction is very important for all the organization since it affects the economy and rise manysocial problems with high costs. There are large number of techniques have been developed to predict thebankruptcy, which helps the decision makers such as investors and financial analysts. One of thebankruptcy prediction models is the hybrid model using Fuzzy C-means clustering and MARS, which usesstatic ratios taken from the bank financial statements for prediction, which has its own theoreticaladvantages. The performance of existing bankruptcy model can be improved by selecting the best featuresdynamically depend on the nature of the firm. This dynamic selection can be accomplished by GeneticAlgorithm and it improves the performance of prediction model. .
An efficient clustering algorithm for partitioning Y-short tandem repeats data
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Seman Ali
2012-10-01
Full Text Available Abstract Background Y-Short Tandem Repeats (Y-STR data consist of many similar and almost similar objects. This characteristic of Y-STR data causes two problems with partitioning: non-unique centroids and local minima problems. As a result, the existing partitioning algorithms produce poor clustering results. Results Our new algorithm, called k-Approximate Modal Haplotypes (k-AMH, obtains the highest clustering accuracy scores for five out of six datasets, and produces an equal performance for the remaining dataset. Furthermore, clustering accuracy scores of 100% are achieved for two of the datasets. The k-AMH algorithm records the highest mean accuracy score of 0.93 overall, compared to that of other algorithms: k-Population (0.91, k-Modes-RVF (0.81, New Fuzzy k-Modes (0.80, k-Modes (0.76, k-Modes-Hybrid 1 (0.76, k-Modes-Hybrid 2 (0.75, Fuzzy k-Modes (0.74, and k-Modes-UAVM (0.70. Conclusions The partitioning performance of the k-AMH algorithm for Y-STR data is superior to that of other algorithms, owing to its ability to solve the non-unique centroids and local minima problems. Our algorithm is also efficient in terms of time complexity, which is recorded as O(km(n-k and considered to be linear.
Parallel Clustering Algorithms for Structured AMR
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Gunney, B T; Wissink, A M; Hysom, D A
2005-10-26
We compare several different parallel implementation approaches for the clustering operations performed during adaptive gridding operations in patch-based structured adaptive mesh refinement (SAMR) applications. Specifically, we target the clustering algorithm of Berger and Rigoutsos (BR91), which is commonly used in many SAMR applications. The baseline for comparison is a simplistic parallel extension of the original algorithm that works well for up to O(10{sup 2}) processors. Our goal is a clustering algorithm for machines of up to O(10{sup 5}) processors, such as the 64K-processor IBM BlueGene/Light system. We first present an algorithm that avoids the unneeded communications of the simplistic approach to improve the clustering speed by up to an order of magnitude. We then present a new task-parallel implementation to further reduce communication wait time, adding another order of magnitude of improvement. The new algorithms also exhibit more favorable scaling behavior for our test problems. Performance is evaluated on a number of large scale parallel computer systems, including a 16K-processor BlueGene/Light system.
Analysis of Stemming Algorithm for Text Clustering
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N.Sandhya
2011-09-01
Full Text Available Text document clustering plays an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters. In Bag of words representation of documents the words that appear in documents often have many morphological variants and in most cases, morphological variants of words have similar semantic interpretations and can be considered as equivalent for the purpose of clustering applications. For this reason, a number of stemming Algorithms, or stemmers, have been developed, which attempt to reduce a word to its stem or root form. Thus, the key terms of a document are represented by stems rather than by the original words. In this work we have studied the impact of stemming algorithm along with four popular similarity measures (Euclidean, cosine, Pearson correlation and extended Jaccard in conjunction with different types of vector representation (boolean, term frequency and term frequency and inverse document frequency on cluster quality. For Clustering documents we have used partitional based clustering technique K Means. Performance is measured against a human-imposed classification of Classic data set. We conducted a number of experiments and used entropy measure to assure statistical significance of results. Cosine, Pearson correlation and extended Jaccard similarities emerge as the best measures to capture human categorization behavior, while Euclidean measures perform poor. After applying the Stemming algorithm Euclidean measure shows little improvement.
High-Performance Broadcasting Algorithms on Cluster
Institute of Scientific and Technical Information of China (English)
舒继武; 魏英霞; 王鼎兴
2004-01-01
In many clusters connected by high-speed communication networks, the exact structure of the underlying communication network and the latency difference between different sending and receiving pairs may be ignored when they broadcast, such as in the approach adopted by the broadcasting method in MPICH,a widely used MPI implementation. However, the underlying network cluster topologies are becoming more and more complicated and the performance of traditional broadcasting algorithms, such as MPICH's MPI_Bcast, is far from good. This paper analyzed the impact of communication latencies and the underlying topologies on the performance of broadcasting algorithms for multilevel clusters. A multilevel model was developed for broadcasting in clusters with complicated topologies, which divides the cluster topology into many levels based on the underlying topology. The multilevel model was used to develop a new broadcast algorithm,MLM broadcast-2 (MLMB-2), that adapts to a wide range of clusters. Comparison of the performance of the counterpart MPI operation MPI_Bcast and MLMB-2 shows that MLMB-2 outperforms MPl_Bcast by decreasing the broadcast running time by 60%-90%.
Cluster Algorithm Special Purpose Processor
Talapov, A. L.; Shchur, L. N.; Andreichenko, V. B.; Dotsenko, Vl. S.
We describe a Special Purpose Processor, realizing the Wolff algorithm in hardware, which is fast enough to study the critical behaviour of 2D Ising-like systems containing more than one million spins. The processor has been checked to produce correct results for a pure Ising model and for Ising model with random bonds. Its data also agree with the Nishimori exact results for spin glass. Only minor changes of the SPP design are necessary to increase the dimensionality and to take into account more complex systems such as Potts models.
Cluster algorithm special purpose processor
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Talapov, A.L.; Shchur, L.N.; Andreichenko, V.B.; Dotsenko, V.S. (Landau Inst. for Theoretical Physics, GSP-1 117940 Moscow V-334 (USSR))
1992-08-10
In this paper, the authors describe a Special Purpose Processor, realizing the Wolff algorithm in hardware, which is fast enough to study the critical behaviour of 2D Ising-like systems containing more than one million spins. The processor has been checked to produce correct results for a pure Ising model and for Ising model with random bonds. Its data also agree with the Nishimori exact results for spin glass. Only minor changes of the SPP design are necessary to increase the dimensionality and to take into account more complex systems such as Potts models.
An Improved Heuristic Ant-Clustering Algorithm
Institute of Scientific and Technical Information of China (English)
Yunfei Chen; Yushu Liu; Jihai Zhao
2004-01-01
An improved heuristic ant-clustering algorithm(HAC)is presented in this paper. A device of ＇memory bank＇ is proposed,which can bring forth heuristic knowledge guiding ant to move in the bi-dimension grid space.The device experiments on real data sets and synthetic data sets.The results demonstrate that HAC has superiority in misclassification error rate and runtime over the classical algorithm.
Multidistribution Center Location Based on Real-Parameter Quantum Evolutionary Clustering Algorithm
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Huaixiao Wang
2014-01-01
Full Text Available To determine the multidistribution center location and the distribution scope of the distribution center with high efficiency, the real-parameter quantum-inspired evolutionary clustering algorithm (RQECA is proposed. RQECA is applied to choose multidistribution center location on the basis of the conventional fuzzy C-means clustering algorithm (FCM. The combination of the real-parameter quantum-inspired evolutionary algorithm (RQIEA and FCM can overcome the local search defect of FCM and make the optimization result independent of the choice of initial values. The comparison of FCM, clustering based on simulated annealing genetic algorithm (CSAGA, and RQECA indicates that RQECA has the same good convergence as CSAGA, but the search efficiency of RQECA is better than that of CSAGA. Therefore, RQECA is more efficient to solve the multidistribution center location problem.
A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection
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Dalton Meitei Thounaojam
2016-01-01
Full Text Available This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter.
Using Quadtree Algorithm for Improving Fuzzy C-means Method in Image Segmentation
Zahra Ghorbanzad; Farshid Babapour
2012-01-01
Image segmentation is an essential processing step for much image application and there are a large number of segmentation techniques. A new algorithm for image segmentation called Quad tree fuzzy c-means (QFCM) is presented I this work. The key idea in our approach is a Quad tree function combined with fuzzy c-means algorithm. In this article we also discuss the advantages and disadvantages of other image segmenting methods like: k-means, c-means, and blocked fuzzy c-means. Different experim...
Fuzzy PID control algorithm based on PSO and application in BLDC motor
Lin, Sen; Wang, Guanglong
2017-06-01
A fuzzy PID control algorithm is studied based on improved particle swarm optimization (PSO) to perform Brushless DC (BLDC) motor control which has high accuracy, good anti-jamming capability and steady state accuracy compared with traditional PID control. The mathematical and simulation model is established for BLDC motor by simulink software, and the speed loop of the fuzzy PID controller is designed. The simulation results show that the fuzzy PID control algorithm based on PSO has higher stability, high control precision and faster dynamic response speed.
A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection.
Thounaojam, Dalton Meitei; Khelchandra, Thongam; Manglem Singh, Kh; Roy, Sudipta
2016-01-01
This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter.
Marwati, Rini; Yulianti, Kartika; Pangestu, Herny Wulandari
2016-02-01
A fuzzy evolutionary algorithm is an integration of an evolutionary algorithm and a fuzzy system. In this paper, we present an application of a genetic algorithm to a fuzzy evolutionary algorithm to detect and to solve chromosomes conflict. A chromosome conflict is identified by existence of any two genes in a chromosome that has the same values as two genes in another chromosome. Based on this approach, we construct an algorithm to solve a lecture scheduling problem. Time codes, lecture codes, lecturer codes, and room codes are defined as genes. They are collected to become chromosomes. As a result, the conflicted schedule turns into chromosomes conflict. Built in the Delphi program, results show that the conflicted lecture schedule problem is solvable by this algorithm.
FUZZY CLUSTERING: APPLICATION ON ORGANIZATIONAL METAPHORS IN BRAZILIAN COMPANIES
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Angel Cobo
2012-08-01
Full Text Available Different theories of organization and management are based on implicit images or metaphors. Nevertheless, a quantitative approach is needed to minimize human subjectivity or bias on metaphors studies. Hence, this paper analyzed the presence of metaphors and clustered them using fuzzy data mining techniques in a sample of 61 Brazilian companies that operate in the state of Rio Grande do Sul. For this purpose the results of a questionnaire answered by 198 employees of companies in the sample were analyzed by R free software. The results show that it is difficult to find a clear image in most organizations. In most cases characteristics of different images or metaphors are observed, so soft computing techniques are particularly appropriate for this type of analysis. However, according to these results, it is noted that the most present image in the organizations studied is that of “organisms” and the least present image is that of a “political system” and of an “instrument of domination”
van der Lee, J H; Svrcek, W Y; Young, B R
2008-01-01
Model Predictive Control is a valuable tool for the process control engineer in a wide variety of applications. Because of this the structure of an MPC can vary dramatically from application to application. There have been a number of works dedicated to MPC tuning for specific cases. Since MPCs can differ significantly, this means that these tuning methods become inapplicable and a trial and error tuning approach must be used. This can be quite time consuming and can result in non-optimum tuning. In an attempt to resolve this, a generalized automated tuning algorithm for MPCs was developed. This approach is numerically based and combines a genetic algorithm with multi-objective fuzzy decision-making. The key advantages to this approach are that genetic algorithms are not problem specific and only need to be adapted to account for the number and ranges of tuning parameters for a given MPC. As well, multi-objective fuzzy decision-making can handle qualitative statements of what optimum control is, in addition to being able to use multiple inputs to determine tuning parameters that best match the desired results. This is particularly useful for multi-input, multi-output (MIMO) cases where the definition of "optimum" control is subject to the opinion of the control engineer tuning the system. A case study will be presented in order to illustrate the use of the tuning algorithm. This will include how different definitions of "optimum" control can arise, and how they are accounted for in the multi-objective decision making algorithm. The resulting tuning parameters from each of the definition sets will be compared, and in doing so show that the tuning parameters vary in order to meet each definition of optimum control, thus showing the generalized automated tuning algorithm approach for tuning MPCs is feasible.
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Guohua Zou
2016-12-01
Full Text Available New medical imaging technology, such as Computed Tomography and Magnetic Resonance Imaging (MRI, has been widely used in all aspects of medical diagnosis. The purpose of these imaging techniques is to obtain various qualitative and quantitative data of the patient comprehensively and accurately, and provide correct digital information for diagnosis, treatment planning and evaluation after surgery. MR has a good imaging diagnostic advantage for brain diseases. However, as the requirements of the brain image definition and quantitative analysis are always increasing, it is necessary to have better segmentation of MR brain images. The FCM (Fuzzy C-means algorithm is widely applied in image segmentation, but it has some shortcomings, such as long computation time and poor anti-noise capability. In this paper, firstly, the Ant Colony algorithm is used to determine the cluster centers and the number of FCM algorithm so as to improve its running speed. Then an improved Markov random field model is used to improve the algorithm, so that its antinoise ability can be improved. Experimental results show that the algorithm put forward in this paper has obvious advantages in image segmentation speed and segmentation effect.
Cause and effect analysis by fuzzy relational equations and a genetic algorithm
Energy Technology Data Exchange (ETDEWEB)
Rotshtein, Alexander P. [Industrial Engineering and Management Department, Jerusalem College of Technology-Machon Lev, Havaad Haleumi St., 21, 91160, Jerusalem (Israel)]. E-mail: rot@jct.ac.il; Posner, Morton [Industrial Engineering and Management Department, Jerusalem College of Technology-Machon Lev, Havaad Haleumi St., 21, 91160, Jerusalem (Israel); Rakytyanska, Hanna B. [Department of Applied Mathematics, Vinnitsa State Technical University, Khmelnitske Sh., 95, 21021, Vinnitsa (Ukraine)]. E-mail: h_rakit@hotmail.com
2006-09-15
This paper proposes using a genetic algorithm as a tool to solve the fault diagnosis problem. The fault diagnosis problem is based on a cause and effect analysis which is formally described by fuzzy relations. Fuzzy relations are formed on the basis of expert assessments. Application of expert fuzzy relations to restore and identify the causes through the observed effects requires the solution to a system of fuzzy relational equations. In this study this search for a solution amounts to solving a corresponding optimization problem. An optimization algorithm is based on the application of genetic operations of crossover, mutation and selection. The genetic algorithm suggested here represents an application in expert systems of fault diagnosis and quality control.
A fuzzy rule based genetic algorithm and its application in FMS
Institute of Scientific and Technical Information of China (English)
Li Shugang; Wu Zhiming; Pang Xiaohong
2005-01-01
Most of the FMS (flexible manufacturing systems) problems belong to NP-hard (non-polynomial hard) problems. The facility layout problem and job-shop schedule problem are such examples. GA (genetic algorithm) is applied to get an optimal solution. However, traditional GAs are usually of low efficiency because of their early convergence. In order to overcome the shortcoming of the GA a fuzzy rule based GA is proposed, in which a fuzzy logical controller is introduced to adjust the value of crossover probability, mutation probability and crossover length. The HGA (hybrid genetic algorithm), which is integrated with a fuzzy logic controller, can avoid premature convergence, and improve the efficiency greatly. Finally, simulation results of the facility layout problem and job-shop schedule problem are given. The results show that the new genetic algorithm integrated with fuzzy logic controller is excellent in searching efficiency.
Novel combinatorial algorithm for the problems of fuzzy grey multi-attribute group decision making
Institute of Scientific and Technical Information of China (English)
Rao Congjun; Xiao Xinping; Peng Jin
2007-01-01
To study the fuzzy and grey information in the problems of multi-attribute group decision making, the basic concepts of both fuzzy grey numbers and grey interval numbers are given firstly, then a new model of fuzzy grey multi-attribute group decision making based on the theories of fuzzy mathematics and grey system is presented. Furthermore, the grey interval relative degree and deviation degree is defined, and both the optimistic algorithm of the grey interval relational degree and the algorithm of deviation degree minimization for solving this new model are also given. Finally, a decision making example to demonstrate the feasibility and rationality of this new method is given, and the results by using these two algorithms are uniform.
A Fast Algorithm for Support Vector Clustering
Institute of Scientific and Technical Information of China (English)
吕常魁; 姜澄宇; 王宁生
2004-01-01
Support Vector Clustering (SVC) is a kernel-based unsupervised learning clustering method. The main drawback of SVC is its high computational complexity in getting the adjacency matrix describing the connectivity for each pairs of points. Based on the proximity graph model[3] , the Euclidean distance in Hilbert space is calculated using a Gaussian kernel, which is the right criterion to generate a minimum spanning tree using Kruskal's algorithm. Then the connectivity estimation is lowered by only checking the linkages between the edges that construct the main stem of the MST (Minimum Spanning Tree), in which the non-compatibility degree is originally defined to support the edge selection during linkage estimations. This new approach is experimentally analyzed.The results show that the revised algorithm has a better performance than the proximity graph model with faster speed, optimized clustering quality and strong ability to noise suppression, which makes SVC scalable to large data sets.
Directory of Open Access Journals (Sweden)
Jiahang Yuan
2017-01-01
Full Text Available In consideration of the interaction among attributes and the influence of decision makers’ risk attitude, this paper proposes an intuitionistic trapezoidal fuzzy aggregation operator based on Choquet integral and prospect theory. With respect to a multiattribute group decision-making problem, the prospect value functions of intuitionistic trapezoidal fuzzy numbers are aggregated by the proposed operator; then a grey relation-projection pursuit dynamic cluster method is developed to obtain the ranking of alternatives; the firefly algorithm is used to optimize the objective function of projection for obtaining the best projection direction of grey correlation projection values, and the grey correlation projection values are evaluated, which are applied to classify, rank, and prefer the alternatives. Finally, an illustrative example is taken in the present study to make the proposed method comprehensible.
Research on the Algorithm of Avionic Device Fault Diagnosis Based on Fuzzy Expert System
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Based on the fuzzy expert system fault diagnosis theory, the knowledge base architecture and inference engine algorithm are put forward for avionic device fault diagnosis. The knowledge base is constructed by fault query network, of which the basic element is the test-diagnosis fault unit. Every underlying fault cause's membership degree is calculated using fuzzy product inference algorithm, and the fault answer best selection algorithm is developed, to which the deep knowledge is applied. Using some examples,the proposed algorithm is analyzed for its capability of synthesis diagnosis and its improvement compared to greater membership degree first principle.
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S.Praveena
2015-06-01
Full Text Available This paper presents a hybrid clustering algorithm and feed-forward neural network classifier for land-cover mapping of trees, shade, building and road. It starts with the single step preprocessing procedure to make the image suitable for segmentation. The pre-processed image is segmented using the hybrid genetic-Artificial Bee Colony(ABC algorithm that is developed by hybridizing the ABC and FCM to obtain the effective segmentation in satellite image and classified using neural network . The performance of the proposed hybrid algorithm is compared with the algorithms like, k-means, Fuzzy C means(FCM, Moving K-means, Artificial Bee Colony(ABC algorithm, ABC-GA algorithm, Moving KFCM and KFCM algorithm.
Fuzzy Activation and Clustering of Nodes in a Hybrid Fibre Network Roll-out
Kraak, J.J.; Phillipson, F.
2015-01-01
To design a Hybrid Fibre network, a selection of nodes is provided with active equipment and connected with fibre. If there is a need for a ring structure for high reliability, the activated nodes need to be clustered. In this paper a fuzzy method is proposed for this activation and clustering probl
New Results in Fuzzy Clustering Based on the Concept of Indistinguishability Relation
1984-01-01
NEW RESULTS IN Fuzzy CLUSTERING BASED ON THE CONCEPT OF INDISTINGUISHABILITY RELATION KEYWORDS R . Lopez de Mantaras Facultat d ’Informatica...Universitat Politecnica de Barcelona Dulcet, 12. Barcelona-34. Spain. L. Valverde* Dept. de Matematiques i Estadistica Universitat Politecnica de... r -cluster that extend Ruspini’s definition (Ruspini, 1982). Our definition is based on the new concept of indis- tinguishability relation (Trillas
Automatic online spike sorting with singular value decomposition and fuzzy C-mean clustering
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Oliynyk Andriy
2012-08-01
Full Text Available Abstract Background Understanding how neurons contribute to perception, motor functions and cognition requires the reliable detection of spiking activity of individual neurons during a number of different experimental conditions. An important problem in computational neuroscience is thus to develop algorithms to automatically detect and sort the spiking activity of individual neurons from extracellular recordings. While many algorithms for spike sorting exist, the problem of accurate and fast online sorting still remains a challenging issue. Results Here we present a novel software tool, called FSPS (Fuzzy SPike Sorting, which is designed to optimize: (i fast and accurate detection, (ii offline sorting and (iii online classification of neuronal spikes with very limited or null human intervention. The method is based on a combination of Singular Value Decomposition for fast and highly accurate pre-processing of spike shapes, unsupervised Fuzzy C-mean, high-resolution alignment of extracted spike waveforms, optimal selection of the number of features to retain, automatic identification the number of clusters, and quantitative quality assessment of resulting clusters independent on their size. After being trained on a short testing data stream, the method can reliably perform supervised online classification and monitoring of single neuron activity. The generalized procedure has been implemented in our FSPS spike sorting software (available free for non-commercial academic applications at the address: http://www.spikesorting.com using LabVIEW (National Instruments, USA. We evaluated the performance of our algorithm both on benchmark simulated datasets with different levels of background noise and on real extracellular recordings from premotor cortex of Macaque monkeys. The results of these tests showed an excellent accuracy in discriminating low-amplitude and overlapping spikes under strong background noise. The performance of our method is
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K. Venkata Subbaiah
2010-01-01
Full Text Available The nodes in the mobile ad hoc networks act as router and host, the routing protocol is the primary issue and has to be supported before any applications can be deployed for mobile ad hoc networks. In recent many research protocols are proposed for finding an efficient route between the nodes. But most of the protocol’s that uses conventional techniques in routing; CBRP is a routing protocol that has a hierarchical-based design. This protocol divides the network area into several smaller areas called cluster. We propose a fuzzy logic based cluster head election using energy concept forcluster head routing protocol in MANET’S. Selecting an appropriate cluster head can save power for the whole mobile ad hoc network. Generally, Cluster Head election for mobile ad hoc network is based on the distance to the centroid of a cluster, and the closest one is elected as the cluster head'; or pick a node with the maximum battery capacity as the cluster head. In this paper, we present a cluster head election scheme using fuzzy logic system (FLS for mobile ad hoc networks. Three descriptors are used: distance of a node to the cluster centroid, its remaining battery capacity, and its degree of mobility. The linguistic knowledge of cluster head election based on these three descriptors is obtained from a group of network experts. 27 FLS rules are set up based on the linguistic knowledge. The output of the FLS provides a cluster head possibility, and node with the highest possibility is elected as the cluster head. The performance of fuzzy cluster head selection is evaluated using simulation, and is compared to LEACH protocol with out fuzzy cluster head election procedures and showed the proposed work is efficient than the previous one.
Teslic, Luka; Hartmann, Benjamin; Nelles, Oliver; Skrjanc, Igor
2011-12-01
This paper deals with the problem of fuzzy nonlinear model identification in the framework of a local model network (LMN). A new iterative identification approach is proposed, where supervised and unsupervised learning are combined to optimize the structure of the LMN. For the purpose of fitting the cluster-centers to the process nonlinearity, the Gustafsson-Kessel (GK) fuzzy clustering, i.e., unsupervised learning, is applied. In combination with the LMN learning procedure, a new incremental method to define the number and the initial locations of the cluster centers for the GK clustering algorithm is proposed. Each data cluster corresponds to a local region of the process and is modeled with a local linear model. Since the validity functions are calculated from the fuzzy covariance matrices of the clusters, they are highly adaptable and thus the process can be described with a very sparse amount of local models, i.e., with a parsimonious LMN model. The proposed method for constructing the LMN is finally tested on a drug absorption spectral process and compared to two other methods, namely, Lolimot and Hilomot. The comparison between the experimental results when using each method shows the usefulness of the proposed identification algorithm.
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Esther A.K James
2012-01-01
Full Text Available Problem statement: The paper addresses the face recognition problem by proposing Weighted Fuzzy Fisherface (WFF technique using Biorthogonal Transformation. The Weighted Fuzzy Fisherface technique is an extension of Fisher Face technique by introducing fuzzy class membership to each training sample in calculating the scatter matrices. Approach: In weighted fuzzy fisherface method, the weight emphasizes classes that are close together and deemphasizes the classes that are far away from each other. Results: The proposed method is more advantageous for the classification task and its accuracy is improved. Also with the performance measures False Acceptance Rate (FAR, False Rejection Rate (FRR and Equal Error Rate (EER are calculated. Conclusion: Weighted fuzzy fisherface algorithm using wavelet transform can effectively and efficiently used for face recognition and its accuracy is improved.
Fuzzy logic and genetic algorithms for intelligent control of structures using MR dampers
Yan, Gang; Zhou, Lily L.
2004-07-01
Fuzzy logic control (FLC) and genetic algorithms (GA) are integrated into a new approach for the semi-active control of structures installed with MR dampers against severe dynamic loadings such as earthquakes. The interactive relationship between the structural response and the input voltage of MR dampers is established by using a fuzzy controller rather than the traditional way by introducing an ideal active control force. GA is employed as an adaptive method for optimization of parameters and for selection of fuzzy rules of the fuzzy control system, respectively. The maximum structural displacement is selected and used as the objective function to be minimized. The objective function is then converted to a fitness function to form the basis of genetic operations, i.e. selection, crossover, and mutation. The proposed integrated architecture is expected to generate an effective and reliable fuzzy control system by GA"s powerful searching and self-learning adaptive capability.
Yan, Gang; Zhou, Lily L.
2006-09-01
This study presents a design strategy based on genetic algorithms (GA) for semi-active fuzzy control of structures that have magnetorheological (MR) dampers installed to prevent damage from severe dynamic loads such as earthquakes. The control objective is to minimize both the maximum displacement and acceleration responses of the structure. Interactive relationships between structural responses and input voltages of MR dampers are established by using a fuzzy controller. GA is employed as an adaptive method for design of the fuzzy controller, which is here known as a genetic adaptive fuzzy (GAF) controller. The multi-objectives are first converted to a fitness function that is used in standard genetic operations, i.e. selection, crossover, and mutation. The proposed approach generates an effective and reliable fuzzy logic control system by powerful searching and self-learning adaptive capabilities of GA. Numerical simulations for single and multiple damper cases are given to show the effectiveness and efficiency of the proposed intelligent control strategy.
Optimal fuzzy PID controller with adjustable factors based on flexible polyhedron search algorithm
Institute of Scientific and Technical Information of China (English)
谭冠政; 肖宏峰; 王越超
2002-01-01
A new kind of optimal fuzzy PID controller is proposed, which contains two parts. One is an on-line fuzzy inference system, and the other is a conventional PID controller. In the fuzzy inference system, three adjustable factors xp, xi, and xd are introduced. Their functions are to further modify and optimize the result of the fuzzy inference so as to make the controller have the optimal control effect on a given object. The optimal values of these adjustable factors are determined based on the ITAE criterion and the Nelder and Mead′s flexible polyhedron search algorithm. This optimal fuzzy PID controller has been used to control the executive motor of the intelligent artificial leg designed by the authors. The result of computer simulation indicates that this controller is very effective and can be widely used to control different kinds of objects and processes.
Application of an iterative method and an evolutionary algorithm in fuzzy optimization
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Ricardo Coelho Silva
2012-08-01
Full Text Available This work develops two approaches based on the fuzzy set theory to solve a class of fuzzy mathematical optimization problems with uncertainties in the objective function and in the set of constraints. The first approach is an adaptation of an iterative method that obtains cut levels and later maximizes the membership function of fuzzy decision making using the bound search method. The second one is a metaheuristic approach that adapts a standard genetic algorithm to use fuzzy numbers. Both approaches use a decision criterion called satisfaction level that reaches the best solution in the uncertain environment. Selected examples from the literature are presented to compare and to validate the efficiency of the methods addressed, emphasizing the fuzzy optimization problem in some import-export companies in the south of Spain.
Design of the Fuzzy Control Systems Based on Genetic Algorithm for Intelligent Robots
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Gyula Mester
2014-07-01
Full Text Available This paper gives the structure optimization of fuzzy control systems based on genetic algorithm in the MATLAB environment. The genetic algorithm is a powerful tool for structure optimization of the fuzzy controllers, therefore, in this paper, integration and synthesis of fuzzy logic and genetic algorithm has been proposed. The genetic algorithms are applied for fuzzy rules set, scaling factors and membership functions optimization. The fuzzy control structure initial consist of the 3 membership functions and 9 rules and after the optimization it is enough for the 4 DOF SCARA Robot control to compensate for structured and unstructured uncertainty. Fuzzy controller with the generalized bell membership functions can provide better dynamic performance of the robot then with the triangular membership functions. The proposed joint-space controller is computationally simple and had adaptability to a sudden change in the dynamics of the robot. Results of the computer simulation applied to the 4 DOF SCARA Robot show the validity of the proposed method.
The upper bound of the optimal number of clusters in fuzzy clustering
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
The upper bound of the optimal number of clusters in clustering algorithm is studied in this paper. A new method is proposed to solve this issue. This method shows that the rule cmax≤n, which is popular in current papers, is reasonable in some sense. The above conclusion is tested and analyzed by some typical examples in the literature, which demonstrates the validity of the new method.
Parallel FFT Algorithm on Computer Clusters
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
DFT is widely applied in the field of signal process and others. Most present rapid ways of calculation are either based on paralleled computers connected by such particular systems like butterfly network, hypercube etc;or based on the assumption of instant transportation, non-conflict communication, complete connection of paralleled processors and unlimited usable processors. However, the delay of communication in the system of information transmission cannot be ignored. This paper works on the following aspects: instant transmission, dispatching missions, and the path of information through the communication link in the computer cluster systems;layout of the dynamic FFT algorithm under the different structures of computer clusters.
Shao, Yuxiang; Chen, Qing; Wei, Zhenhua
Logistics distribution center location evaluation is a dynamic, fuzzy, open and complicated nonlinear system, which makes it difficult to evaluate the distribution center location by the traditional analysis method. The paper proposes a distribution center location evaluation system which uses the fuzzy neural network combined with the genetic algorithm. In this model, the neural network is adopted to construct the fuzzy system. By using the genetic algorithm, the parameters of the neural network are optimized and trained so as to improve the fuzzy system’s abilities of self-study and self-adaptation. At last, the sampled data are trained and tested by Matlab software. The simulation results indicate that the proposed identification model has very small errors.
A genetic algorithms approach for altering the membership functions in fuzzy logic controllers
Shehadeh, Hana; Lea, Robert N.
1992-01-01
Through previous work, a fuzzy control system was developed to perform translational and rotational control of a space vehicle. This problem was then re-examined to determine the effectiveness of genetic algorithms on fine tuning the controller. This paper explains the problems associated with the design of this fuzzy controller and offers a technique for tuning fuzzy logic controllers. A fuzzy logic controller is a rule-based system that uses fuzzy linguistic variables to model human rule-of-thumb approaches to control actions within a given system. This 'fuzzy expert system' features rules that direct the decision process and membership functions that convert the linguistic variables into the precise numeric values used for system control. Defining the fuzzy membership functions is the most time consuming aspect of the controller design. One single change in the membership functions could significantly alter the performance of the controller. This membership function definition can be accomplished by using a trial and error technique to alter the membership functions creating a highly tuned controller. This approach can be time consuming and requires a great deal of knowledge from human experts. In order to shorten development time, an iterative procedure for altering the membership functions to create a tuned set that used a minimal amount of fuel for velocity vector approach and station-keep maneuvers was developed. Genetic algorithms, search techniques used for optimization, were utilized to solve this problem.
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Tonatiuh Hernández Cortés
2015-01-01
Full Text Available The synchronization of chaotic systems, described by discrete-time T-S fuzzy models, is treated by means of fuzzy output regulation theory. The conditions for designing a discrete-time output regulator are given in this paper. Besides, when the system does not fulfill the conditions for exact tracking, a new regulator based on genetic algorithms is considered. The genetic algorithms are used to approximate the adequate membership functions, which allow the adequate combination of local regulators. As a result, the tracking error is significantly reduced. Both the Complete Synchronization and the Generalized Synchronization problem are studied. Some numerical examples are used to illustrate the effectiveness of the proposed approach.
Ant colony optimization algorithm and its application to Neuro-Fuzzy controller design
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
An adaptive ant colony algorithm is proposed based on dynamically adjusting the strategy of updating trail information.The algorithm can keep good balance between accelerating convergence and averting precocity and stagnation.The results of function optimization show that the algorithm has good searching ability and high convergence speed.The algorithm is employed to design a neuro-fuzzy controller for real-time control of an inverted pendulum.In order to avoid the combinatorial explosion of fuzzy.rules due to multivariable inputs,a state variable synthesis scheme is emploved to reduce the number of fuzzy rules greatly.The simulation results show that the designed controller can control the inverted pendulum successfully.
The Fuzzy Modeling Algorithm for Complex Systems Based on Stochastic Neural Network
Institute of Scientific and Technical Information of China (English)
李波; 张世英; 李银惠
2002-01-01
A fuzzy modeling method for complex systems is studied. The notation of general stochastic neural network (GSNN) is presented and a new modeling method is given based on the combination of the modified Takagi and Sugeno's(MTS) fuzzy model and one-order GSNN. Using expectation-maximization (EM) algorithm, parameter estimation and model selection procedures are given. It avoids the shortcomings brought by other methods such as BP algorithm, when the number of parameters is large, BP algorithm is still difficult to apply directly without fine tuning and subjective tinkering. Finally, the simulated example demonstrates the effectiveness.
Dominance-based Matrix algorithm for Knowledge Reductions in Incomplete Fuzzy Information System
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Lixin Fan
2013-07-01
Full Text Available In this paper，definitions of knowledge granulation and rough entropy are proposed based on dominance relations in incomplete fuzzy information system, and important properties are obtained. It can be found that using the definitions can measure uncertainty of an attribute set in the incomplete fuzzy information systems. A matrix algorithm for attributes reduction is acquired in the systems. An example illustrates the validity of this algorithm, and results of compared with other existing methods show that the algorithm is an efficient tool for data mining.
Comparative study of several Clustering Algorithms
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Prof. Neha Soni, Dr. Amit Ganatra
2012-12-01
Full Text Available Cluster Analysis is a process of grouping theobjects, where objects can be physical like a studentor can be an abstract such as behaviour of acustomer or handwriting of a person. The clusteranalysis is as old as a human life and has its rootsin many fields such as statistics, machine learning,biology, artificial intelligence. It is an unsupervisedlearning and faces many challenges such as a highdimension of the dataset, arbitrary shapes ofclusters, scalability, input parameter, domainknowledge and noisy data. Large number ofclustering algorithms had been proposed till date toaddress these challenges. There do not exist a singlealgorithm which can adequately handle all sorts ofrequirement. This makes a great challenge for theuser to do selection among the available algorithmfor the specific task. The purpose of this paper is toprovide a detailed analytical comparison of some ofthe very well known clustering algorithms, whichprovides guidance for the selection of clusteringalgorithm for a specific application.
A Genetic Algorithm for Single Machine Scheduling with Fuzzy Processing Time and Multiple Objectives
Institute of Scientific and Technical Information of China (English)
吴超超; 顾幸生
2004-01-01
In this paper, by considering the fuzzy nature of the data in real-life problems, single machine scheduling problems with fuzzy processing time and multiple objectives are formulated and an efficient genetic algorithm which is suitable for solving these problems is proposed. As illustrative numerical examples, twenty jobs processing on a machine is considered. The feasibility and effectiveness of the proposed method have been demonstrated in the simulation.
CABOSFV algorithm for high dimensional sparse data clustering
Institute of Scientific and Technical Information of China (English)
Sen Wu; Xuedong Gao
2004-01-01
An algorithm, Clustering Algorithm Based On Sparse Feature Vector (CABOSFV), was proposed for the high dimensional clustering of binary sparse data. This algorithm compresses the data effectively by using a tool 'Sparse Feature Vector', thus reduces the data scale enormously, and can get the clustering result with only one data scan. Both theoretical analysis and empirical tests showed that CABOSFV is of low computational complexity. The algorithm finds clusters in high dimensional large datasets efficiently and handles noise effectively.
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Juan Carlos Figueroa García
2011-12-01
The presented approach uses an iterative algorithm which finds stable solutions to problems with fuzzy parameter sinboth sides of an FLP problem. The algorithm is based on the soft constraints method proposed by Zimmermann combined with an iterative procedure which gets a single optimal solution.
First Cluster Algorithm Special Purpose Processor
Talapov, A. L.; Andreichenko, V. B.; Dotsenko S., Vi.; Shchur, L. N.
We describe the architecture of the special purpose processor built to realize in hardware cluster Wolff algorithm, which is not hampered by a critical slowing down. The processor simulates two-dimensional Ising-like spin systems. With minor changes the same very effective architecture, which can be defined as a Memory Machine, can be used to study phase transitions in a wide range of models in two or three dimensions.
Dynamic exponents for potts model cluster algorithms
Coddington, Paul D.; Baillie, Clive F.
We have studied the Swendsen-Wang and Wolff cluster update algorithms for the Ising model in 2, 3 and 4 dimensions. The data indicate simple relations between the specific heat and the Wolff autocorrelations, and between the magnetization and the Swendsen-Wang autocorrelations. This implies that the dynamic critical exponents are related to the static exponents of the Ising model. We also investigate the possibility of similar relationships for the Q-state Potts model.
Enhanced Unequal Clustering Algorithm for Wireless Sensor Networks
Talbi, Said; Zaouche, Lotfi
2015-01-01
International audience; Clustering is considered as solution for more energy conservation during communications in wireless sensor networks. Recently, a new clustering algorithm named Unequal Clustering Algorithm (UCA) is proposed to avoid the burdened cluster-heads located around the sink due to the traffic coming from others which are far to the base station. This paper presents an Enhanced Unequal Clustering Algorithm called EUCA. This solution reduces the control traffic during a clusteri...
Assessment of Heart Disease using Fuzzy Classification Techniques
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Horia F. Pop
2001-01-01
Full Text Available In this paper we discuss the classification results of cardiac patients of ischemical cardiopathy, valvular heart disease, and arterial hypertension, based on 19 characteristics (descriptors including ECHO data, effort testings, and age and weight. In this order we have used different fuzzy clustering algorithms, namely hierarchical fuzzy clustering, hierarchical and horizontal fuzzy characteristics clustering, and a new clustering technique, fuzzy hierarchical cross-classification. The characteristics clustering techniques produce fuzzy partitions of the characteristics involved and, thus, are useful tools for studying the similarities between different characteristics and for essential characteristics selection. The cross-classification algorithm produces not only a fuzzy partition of the cardiac patients analyzed, but also a fuzzy partition of their considered characteristics. In this way it is possible to identify which characteristics are responsible for the similarities or dissimilarities observed between different groups of patients.
Nagoor Gani, A; Latha, S R
2016-01-01
A Hamiltonian cycle in a graph is a cycle that visits each node/vertex exactly once. A graph containing a Hamiltonian cycle is called a Hamiltonian graph. There have been several researches to find the number of Hamiltonian cycles of a Hamilton graph. As the number of vertices and edges grow, it becomes very difficult to keep track of all the different ways through which the vertices are connected. Hence, analysis of large graphs can be efficiently done with the assistance of a computer system that interprets graphs as matrices. And, of course, a good and well written algorithm will expedite the analysis even faster. The most convenient way to quickly test whether there is an edge between two vertices is to represent graphs using adjacent matrices. In this paper, a new algorithm is proposed to find fuzzy Hamiltonian cycle using adjacency matrix and the degree of the vertices of a fuzzy graph. A fuzzy graph structure is also modeled to illustrate the proposed algorithms with the selected air network of Indigo airlines.
a fuzzy homomorphic algori omomorphic algorithm for image ...
African Journals Online (AJOL)
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nalysis of a novel Fuzzy Homomorphic image enhancement technique is p garithmic ... c Filtering, Illumination correction, Low Contrast Images, Enhancement, F g method is ..... Higher FE values mean more membership values are closer to 1 ...
DEFF Research Database (Denmark)
Nadernejad, Ehsan; Sharifzadeh, Sara
2013-01-01
In this paper, a new pixon-based method is presented for image segmentation. In the proposed algorithm, bilateral filtering is used as a kernel function to form a pixonal image. Using this filter reduces the noise and smoothes the image slightly. By using this pixon-based method, the image over s...... the hierarchical clustering method (Fuzzy C-means algorithm). The experimental results show that the proposed pixon-based approach has a reduced computational load and a better accuracy compared to the other existing pixon-based image segmentation techniques.......In this paper, a new pixon-based method is presented for image segmentation. In the proposed algorithm, bilateral filtering is used as a kernel function to form a pixonal image. Using this filter reduces the noise and smoothes the image slightly. By using this pixon-based method, the image over...
模糊推理基本算法的数学基础%Mathematical Foundation of Basic Algorithms of Fuzzy Reasoning
Institute of Scientific and Technical Information of China (English)
潘正华
2005-01-01
Algorithm of fuzzy reasoning has been successful applied in fuzzy control, but its theoretical foundation of algorithms has not been thoroughly investigated. In this paper, structure of basic algorithms of fuzzy reasoning was studied, its rationality was discussed from the viewpoint of logic and mathematics, and three theorems were proved. These theorems shows that there always exists a mathematical relation (that is, a bounded real function) between the premises and the conclusion for fuzzy reasoning, and in fact various algorithms of fuzzy reasoning are specific forms of this function. Thus these results show that algorithms of fuzzy reasoning are theoretically reliable.
Yu, Hang; Xu, Luping; Feng, Dongzhu; He, Xiaochuan
2015-01-01
Synthetic aperture radar (SAR) image segmentation is investigated from feature extraction to algorithm design, which is characterized by two aspects: (1) multiple heterogeneous features are extracted to describe SAR images and the corresponding similarity measures are developed independently to avoid the mutual influences between different features in order to enhance the discriminability of the final similarity between objects. (2) A method called fuzzy clustering based on independent subspace iterative optimization (FCISIO) is proposed. FCISIO integrates multiple features into an objective function which is then iteratively optimized in each feature subspace to obtain final segmentation results. This strategy can protect the distribution structures of the data points in each feature subspace, which realizes an effective way to integrate multiple features of different properties. In order to improve the computation speed and the accuracy of feature description for FCISIO, we design a region merging algorithm before FCISIO which can use many kinds of information to quickly merge regions inside the true segments. Experiments on synthetic and real SAR images show that the proposed method is effective and robust and can obtain good segmentation results with a very short running time.
ITS Cluster Finding Algorithm on GPU
Changaival, Boonyarit
2014-01-01
ITS cluster finding algorithm is one of the data reduction algorithms at ALICE. It needs to be processed fast due to a high amount of data readout from the detector. A variety of platforms were studied for the system design. My work is to design, implement and benchmark this algorithm on a GPU platform. GPU is one of many platform that promote parallel computing. A high-end GPU can contain over 2000 processing cores comparing to the commodity CPUs which have only four cores. The program is written in C and CUDA library. The throughput (Number of events per second) is used as a metric to measure the performance. With the latest implementation, the throughput was increased by a factor of 5.
Path planning of decentralized multi-quadrotor based on fuzzy-cell decomposition algorithm
Iswanto, Wahyunggoro, Oyas; Cahyadi, Adha Imam
2017-04-01
The paper aims to present a design algorithm for multi quadrotor lanes in order to move towards the goal quickly and avoid obstacles in an area with obstacles. There are several problems in path planning including how to get to the goal position quickly and avoid static and dynamic obstacles. To overcome the problem, therefore, the paper presents fuzzy logic algorithm and fuzzy cell decomposition algorithm. Fuzzy logic algorithm is one of the artificial intelligence algorithms which can be applied to robot path planning that is able to detect static and dynamic obstacles. Cell decomposition algorithm is an algorithm of graph theory used to make a robot path map. By using the two algorithms the robot is able to get to the goal position and avoid obstacles but it takes a considerable time because they are able to find the shortest path. Therefore, this paper describes a modification of the algorithms by adding a potential field algorithm used to provide weight values on the map applied for each quadrotor by using decentralized controlled, so that the quadrotor is able to move to the goal position quickly by finding the shortest path. The simulations conducted have shown that multi-quadrotor can avoid various obstacles and find the shortest path by using the proposed algorithms.
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Orlov A. I.
2016-05-01
Full Text Available Fuzzy sets are the special form of objects of nonnumeric nature. Therefore, in the processing of the sample, the elements of which are fuzzy sets, a variety of methods for the analysis of statistical data of any nature can be used - the calculation of the average, non-parametric density estimators, construction of diagnostic rules, etc. We have told about the development of our work on the theory of fuzziness (1975 - 2015. In the first of our work on fuzzy sets (1975, the theory of random sets is regarded as a generalization of the theory of fuzzy sets. In non-fiction series "Mathematics. Cybernetics" (publishing house "Knowledge" in 1980 the first book by a Soviet author fuzzy sets is published - our brochure "Optimization problems and fuzzy variables". This book is essentially a "squeeze" our research of 70-ies, ie, the research on the theory of stability and in particular on the statistics of objects of non-numeric nature, with a bias in the methodology. The book includes the main results of the fuzzy theory and its note to the random set theory, as well as new results (first publication! of statistics of fuzzy sets. On the basis of further experience, you can expect that the theory of fuzzy sets will be more actively applied in organizational and economic modeling of industry management processes. We discuss the concept of the average value of a fuzzy set. We have considered a number of statements of problems of testing statistical hypotheses on fuzzy sets. We have also proposed and justified some algorithms for restore relationships between fuzzy variables; we have given the representation of various variants of fuzzy cluster analysis of data and variables and described some methods of collection and description of fuzzy data
The Balanced Cross-Layer Design Routing Algorithm in Wireless Sensor Networks Using Fuzzy Logic
Li, Ning; Martínez, José-Fernán; Díaz, Vicente Hernández
2015-01-01
Recently, the cross-layer design for the wireless sensor network communication protocol has become more and more important and popular. Considering the disadvantages of the traditional cross-layer routing algorithms, in this paper we propose a new fuzzy logic-based routing algorithm, named the Balanced Cross-layer Fuzzy Logic (BCFL) routing algorithm. In BCFL, we use the cross-layer parameters’ dispersion as the fuzzy logic inference system inputs. Moreover, we give each cross-layer parameter a dynamic weight according the value of the dispersion. For getting a balanced solution, the parameter whose dispersion is large will have small weight, and vice versa. In order to compare it with the traditional cross-layer routing algorithms, BCFL is evaluated through extensive simulations. The simulation results show that the new routing algorithm can handle the multiple constraints without increasing the complexity of the algorithm and can achieve the most balanced performance on selecting the next hop relay node. Moreover, the Balanced Cross-layer Fuzzy Logic routing algorithm can adapt to the dynamic changing of the network conditions and topology effectively. PMID:26266412
The Balanced Cross-Layer Design Routing Algorithm in Wireless Sensor Networks Using Fuzzy Logic.
Li, Ning; Martínez, José-Fernán; Hernández Díaz, Vicente
2015-08-10
Recently, the cross-layer design for the wireless sensor network communication protocol has become more and more important and popular. Considering the disadvantages of the traditional cross-layer routing algorithms, in this paper we propose a new fuzzy logic-based routing algorithm, named the Balanced Cross-layer Fuzzy Logic (BCFL) routing algorithm. In BCFL, we use the cross-layer parameters' dispersion as the fuzzy logic inference system inputs. Moreover, we give each cross-layer parameter a dynamic weight according the value of the dispersion. For getting a balanced solution, the parameter whose dispersion is large will have small weight, and vice versa. In order to compare it with the traditional cross-layer routing algorithms, BCFL is evaluated through extensive simulations. The simulation results show that the new routing algorithm can handle the multiple constraints without increasing the complexity of the algorithm and can achieve the most balanced performance on selecting the next hop relay node. Moreover, the Balanced Cross-layer Fuzzy Logic routing algorithm can adapt to the dynamic changing of the network conditions and topology effectively.
Fuzzy Classification of Ocean Color Satellite Data for Bio-optical Algorithm Constituent Retrievals
Campbell, Janet W.
1998-01-01
The ocean has been traditionally viewed as a 2 class system. Morel and Prieur (1977) classified ocean water according to the dominant absorbent particle suspended in the water column. Case 1 is described as having a high concentration of phytoplankton (and detritus) relative to other particles. Conversely, case 2 is described as having inorganic particles such as suspended sediments in high concentrations. Little work has gone into the problem of mixing bio-optical models for these different water types. An approach is put forth here to blend bio-optical algorithms based on a fuzzy classification scheme. This scheme involves two procedures. First, a clustering procedure identifies classes and builds class statistics from in-situ optical measurements. Next, a classification procedure assigns satellite pixels partial memberships to these classes based on their ocean color reflectance signature. These membership assignments can be used as the basis for a weighting retrievals from class-specific bio-optical algorithms. This technique is demonstrated with in-situ optical measurements and an image from the SeaWiFS ocean color satellite.
Girola Schneider, R.
2017-07-01
The fuzzy logic is a branch of the artificial intelligence founded on the concept that everything is a matter of degree. It intends to create mathematical approximations on the resolution of certain types of problems. In addition, it aims to produce exact results obtained from imprecise data, for which it is particularly useful for electronic and computer applications. This enables it to handle vague or unspecific information when certain parts of a system are unknown or ambiguous and, therefore, they cannot be measured in a reliable manner. Also, when the variation of a variable can produce an alteration on the others The main focus of this paper is to prove the importance of these techniques formulated from a theoretical analysis on its application on ambiguous situations in the field of the rich clusters of galaxies. The purpose is to show its applicability in the several classification systems proposed for the rich clusters, which are based on criteria such as the level of richness of the cluster, the distribution of the brightest galaxies, whether there are signs of type-cD galaxies or not or the existence of sub-clusters. Fuzzy logic enables the researcher to work with "imprecise" information implementing fuzzy sets and combining rules to define actions. The control systems based on fuzzy logic join input variables that are defined in terms of fuzzy sets through rule groups that produce one or several output values of the system under study. From this context, the application of the fuzzy logic's techniques approximates the solution of the mathematical models in abstractions about the rich galaxy cluster classification of physical properties in order to solve the obscurities that must be confronted by an investigation group in order to make a decision.
Santiago Girola Schneider, Rafael
2015-08-01
The fuzzy logic is a branch of the artificial intelligence founded on the concept that 'everything is a matter of degree.' It intends to create mathematical approximations on the resolution of certain types of problems. In addition, it aims to produce exact results obtained from imprecise data, for which it is particularly useful for electronic and computer applications. This enables it to handle vague or unspecific information when certain parts of a system are unknown or ambiguous and, therefore, they cannot be measured in a reliable manner. Also, when the variation of a variable can produce an alteration on the others.The main focus of this paper is to prove the importance of these techniques formulated from a theoretical analysis on its application on ambiguous situations in the field of the rich clusters of galaxies. The purpose is to show its applicability in the several classification systems proposed for the rich clusters, which are based on criteria such as the level of richness of the cluster, the distribution of the brightest galaxies, whether there are signs of type-cD galaxies or not or the existence of sub-clusters.Fuzzy logic enables the researcher to work with “imprecise” information implementing fuzzy sets and combining rules to define actions. The control systems based on fuzzy logic join input variables that are defined in terms of fuzzy sets through rule groups that produce one or several output values of the system under study. From this context, the application of the fuzzy logic’s techniques approximates the solution of the mathematical models in abstractions about the rich galaxy cluster classification of physical properties in order to solve the obscurities that must be confronted by an investigation group in order to make a decision.
Zhang, D.; Zhang, W. Y.
2017-08-01
Evacuation planning is an important activity in disaster management. It has to be planned in advance due to the unpredictable occurrence of disasters. It is necessary that the evacuation plans are as close as possible to the real evacuation work. However, the evacuation plan is extremely challenging because of the inherent uncertainty of the required information. There is a kind of vehicle routing problem based on the public traffic evacuation. In this paper, the demand for each evacuation set point is a fuzzy number, and each routing selection of the point is based on the fuzzy credibility preference index. This paper proposes an approximate optimal solution for this problem by the genetic algorithm based on the fuzzy reliability theory. Finally, the algorithm is applied to an optimization model, and the experiment result shows that the algorithm is effective.
Intelligent control a hybrid approach based on fuzzy logic, neural networks and genetic algorithms
Siddique, Nazmul
2014-01-01
Intelligent Control considers non-traditional modelling and control approaches to nonlinear systems. Fuzzy logic, neural networks and evolutionary computing techniques are the main tools used. The book presents a modular switching fuzzy logic controller where a PD-type fuzzy controller is executed first followed by a PI-type fuzzy controller thus improving the performance of the controller compared with a PID-type fuzzy controller. The advantage of the switching-type fuzzy controller is that it uses one rule-base thus minimises the rule-base during execution. A single rule-base is developed by merging the membership functions for change of error of the PD-type controller and sum of error of the PI-type controller. Membership functions are then optimized using evolutionary algorithms. Since the two fuzzy controllers were executed in series, necessary further tuning of the differential and integral scaling factors of the controller is then performed. Neural-network-based tuning for the scaling parameters of t...
Performance evaluation of simple linear iterative clustering algorithm on medical image processing.
Cong, Jinyu; Wei, Benzheng; Yin, Yilong; Xi, Xiaoming; Zheng, Yuanjie
2014-01-01
Simple Linear Iterative Clustering (SLIC) algorithm is increasingly applied to different kinds of image processing because of its excellent perceptually meaningful characteristics. In order to better meet the needs of medical image processing and provide technical reference for SLIC on the application of medical image segmentation, two indicators of boundary accuracy and superpixel uniformity are introduced with other indicators to systematically analyze the performance of SLIC algorithm, compared with Normalized cuts and Turbopixels algorithm. The extensive experimental results show that SLIC is faster and less sensitive to the image type and the setting superpixel number than other similar algorithms such as Turbopixels and Normalized cuts algorithms. And it also has a great benefit to the boundary recall, the robustness of fuzzy boundary, the setting superpixel size and the segmentation performance on medical image segmentation.
Designing Integrated Fuzzy Guidance Law for Aerodynamic Homing Missiles Using Genetic Algorithms
Omar, Hanafy M.
The Fuzzy logic controller (FLC) is well-known for robustness to parameter variations and ability to reject noise. However, its design requires definition of many parameters. This work proposes a systematic and simple procedure to develop an integrated fuzzy-based guidance law which consists of three FLC. Each is activated in a region of the interception. Another fuzzy-based switching system is introduced to allow smooth transition between these controllers. The parameters of all the fuzzy controllers, which include the distribution of the membership functions and the rules, are obtained simply by observing the function of each controller. Furthermore, these parameters are tuned by genetic algorithms by solving an optimization problem to minimize the interception time, missile acceleration commands, and miss distance. The simulation results show that the proposed procedure can generate a guidance law with satisfactory performance.
Han, Honggui; Wu, Xiao-Long; Qiao, Jun-Fei
2014-04-01
In this paper, a self-organizing fuzzy-neural-network with adaptive computation algorithm (SOFNN-ACA) is proposed for modeling a class of nonlinear systems. This SOFNN-ACA is constructed online via simultaneous structure and parameter learning processes. In structure learning, a set of fuzzy rules can be self-designed using an information-theoretic methodology. The fuzzy rules with high spiking intensities (SI) are divided into new ones. And the fuzzy rules with a small relative mutual information (RMI) value will be pruned in order to simplify the FNN structure. In parameter learning, the consequent part parameters are learned through the use of an ACA that incorporates an adaptive learning rate strategy into the learning process to accelerate the convergence speed. Then, the convergence of SOFNN-ACA is analyzed. Finally, the proposed SOFNN-ACA is used to model nonlinear systems. The modeling results demonstrate that this proposed SOFNN-ACA can model nonlinear systems effectively.
An Extended Membrane System with Active Membranes to Solve Automatic Fuzzy Clustering Problems.
Peng, Hong; Wang, Jun; Shi, Peng; Pérez-Jiménez, Mario J; Riscos-Núñez, Agustín
2016-05-01
This paper focuses on automatic fuzzy clustering problem and proposes a novel automatic fuzzy clustering method that employs an extended membrane system with active membranes that has been designed as its computing framework. The extended membrane system has a dynamic membrane structure; since membranes can evolve, it is particularly suitable for processing the automatic fuzzy clustering problem. A modification of a differential evolution (DE) mechanism was developed as evolution rules for objects according to membrane structure and object communication mechanisms. Under the control of both the object's evolution-communication mechanism and the membrane evolution mechanism, the extended membrane system can effectively determine the most appropriate number of clusters as well as the corresponding optimal cluster centers. The proposed method was evaluated over 13 benchmark problems and was compared with four state-of-the-art automatic clustering methods, two recently developed clustering methods and six classification techniques. The comparison results demonstrate the superiority of the proposed method in terms of effectiveness and robustness.
Hearing the clusters in a graph: A distributed algorithm
Sahai, Tuhin; Banaszuk, Andrzej
2009-01-01
We propose a novel distributed algorithm to decompose graphs or cluster data. The algorithm recovers the solution obtained from spectral clustering without need for expensive eigenvalue/ eigenvector computations. We demonstrate that by solving the wave equation on the graph, every node can assign itself to a cluster by performing a local fast Fourier transform. We prove the equivalence of our algorithm to spectral clustering, derive convergence rates and demonstrate it on examples.
A High-Order CFS Algorithm for Clustering Big Data
Fanyu Bu; Zhikui Chen; Peng Li; Tong Tang; Ying Zhang
2016-01-01
With the development of Internet of Everything such as Internet of Things, Internet of People, and Industrial Internet, big data is being generated. Clustering is a widely used technique for big data analytics and mining. However, most of current algorithms are not effective to cluster heterogeneous data which is prevalent in big data. In this paper, we propose a high-order CFS algorithm (HOCFS) to cluster heterogeneous data by combining the CFS clustering algorithm and the dropout deep learn...
Improvement and Parallelism of k-Means Clustering Algorithm
Institute of Scientific and Technical Information of China (English)
TIAN Jinlan; ZHU Lin; ZHANG Suqin; LIU Lu
2005-01-01
The k-means clustering algorithm is one of the most commonly used algorithms for clustering analysis. The traditional k-means algorithm is, however, inefficient while working on large numbers of data sets and improving the algorithm efficiency remains a problem. This paper focuses on the efficiency issues of cluster algorithms. A refined initial cluster centers method is designed to reduce the number of iterative procedures in the algorithm. A parallel k-means algorithm is also studied for the problem of the operation limitation of a single processor machine when given huge data sets. The analytical results demonstrate that these improvements can greatly enhance the efficiency of the k-means algorithm, i.e., allow the grouping of a large number of data sets more accurately and more quickly. The analysis has theoretical and practical importance for work on the improvement and parallelism of cluster algorithms.
Lewandowski, Michał; Przybylski, Andrzej; Kuźmicz, Wiesław; Szwed, Hanna
2013-09-01
The aim of the study was to analyze the value of a completely new fuzzy logic-based detection algorithm (FA) in comparison with arrhythmia classification algorithms used in existing ICDs in order to demonstrate whether the rate of inappropriate therapies can be reduced. On the basis of the RR intervals database containing arrhythmia events and controls recordings from the ICD memory a diagnostic algorithm was developed and tested by a computer program. This algorithm uses the same input signals as existing ICDs: RR interval as the primary input variable and two variables derived from it, onset and stability. However, it uses 15 fuzzy rules instead of fixed thresholds used in existing devices. The algorithm considers 6 diagnostic categories: (1) VF (ventricular fibrillation), (2) VT (ventricular tachycardia), (3) ST (sinus tachycardia), (4) DAI (artifacts and heart rhythm irregularities including extrasystoles and T-wave oversensing-TWOS), (5) ATF (atrial and supraventricular tachycardia or fibrillation), and 96) NT (sinus rhythm). This algorithm was tested on 172 RR recordings from different ICDs in the follow-up of 135 patients. All diagnostic categories of the algorithm were present in the analyzed recordings: VF (n = 35), VT (n = 48), ST (n = 14), DAI (n = 32), ATF (n = 18), NT (n = 25). Thirty-eight patients (31.4%) in the studied group received inappropriate ICD therapies. In all these cases the final diagnosis of the algorithm was correct (19 cases of artifacts, 11 of atrial fibrillation and 8 of ST) and fuzzy rules algorithm implementation would have withheld unnecessary therapies. Incidence of inappropriate therapies: 3 vs. 38 (the proposed algorithm vs. ICD diagnosis, respectively) differed significantly (p fuzzy logic based algorithm seems to be promising and its implementation could diminish ICDs inappropriate therapies. We found FA usefulness in correct diagnosis of sinus tachycardia, atrial fibrillation and artifacts in comparison with tested ICDs.
Design of adaptive fuzzy logic controller based on linguistic-hedge concepts and genetic algorithms.
Liu, B D; Chen, C Y; Tsao, J Y
2001-01-01
In this paper, we propose a novel fuzzy logic controller, called linguistic hedge fuzzy logic controller, to simplify the membership function constructions and the rule developments. The design methodology of linguistic hedge fuzzy logic controller is a hybrid model based on the concepts of the linguistic hedges and the genetic algorithms. The linguistic hedge operators are used to adjust the shape of the system membership functions dynamically, and ran speed up the control result to fit the system demand. The genetic algorithms are adopted to search the optimal linguistic hedge combination in the linguistic hedge module, According to the proposed methodology, the linguistic hedge fuzzy logic controller has the following advantages: 1) it needs only the simple-shape membership functions rather than the carefully designed ones for characterizing the related variables; 2) it is sufficient to adopt a fewer number of rules for inference; 3) the rules are developed intuitionally without heavily depending on the endeavor of experts; 4) the linguistic hedge module associated with the genetic algorithm enables it to be adaptive; 5) it performs better than the conventional fuzzy logic controllers do; and 6) it can be realized with low design complexity and small hardware overhead. Furthermore, the proposed approach has been applied to design three well-known nonlinear systems. The simulation and experimental results demonstrate the effectiveness of this design.
A reliable routing algorithm based on fuzzy Petri net in mobile ad hoc networks
Institute of Scientific and Technical Information of China (English)
HU Zhi-gang; MA Hao; WANG Guo-jun; LIAO Lin
2005-01-01
A novel reliable routing algorithm in mobile ad hoc networks using fuzzy Petri net with its reasoning mechanism was proposed to increase the reliability during the routing selection. The algorithm allows the structured representation of network topology, which has a fuzzy reasoning mechanism for finding the routing sprouting tree from the source node to the destination node in the mobile ad hoc environment. Finally, by comparing the degree of reliability in the routing sprouting tree, the most reliable route can be computed. The algorithm not only offers the local reliability between each neighboring node, but also provides global reliability for the whole selected route. The algorithm can be applied to most existing on-demand routing protocols, and the simulation results show that the routing reliability is increased by more than 80% when applying the proposed algorithm to the ad hoc on demand distance vector routing protocol.
Nie, Haitao; Long, Kehui; Ma, Jun; Yue, Dan; Liu, Jinguo
2015-01-01
Partial occlusions, large pose variations, and extreme ambient illumination conditions generally cause the performance degradation of object recognition systems. Therefore, this paper presents a novel approach for fast and robust object recognition in cluttered scenes based on an improved scale invariant feature transform (SIFT) algorithm and a fuzzy closed-loop control method. First, a fast SIFT algorithm is proposed by classifying SIFT features into several clusters based on several attributes computed from the sub-orientation histogram (SOH), in the feature matching phase only features that share nearly the same corresponding attributes are compared. Second, a feature matching step is performed following a prioritized order based on the scale factor, which is calculated between the object image and the target object image, guaranteeing robust feature matching. Finally, a fuzzy closed-loop control strategy is applied to increase the accuracy of the object recognition and is essential for autonomous object manipulation process. Compared to the original SIFT algorithm for object recognition, the result of the proposed method shows that the number of SIFT features extracted from an object has a significant increase, and the computing speed of the object recognition processes increases by more than 40%. The experimental results confirmed that the proposed method performs effectively and accurately in cluttered scenes.
Centralized Fuzzy Data Association Algorithm of Three-sensor Multi-target Tracking System
Directory of Open Access Journals (Sweden)
Zhaofeng Su
2014-02-01
Full Text Available For improving the effect of multi-target tracking in dense target and clutter scenario, a centralized fuzzy optimal assignment algorithm (CMS-FOA of three-sensor multi-target system is proposed. And on the base of this, a generalized probabilistic data association algorithm (CMS-FOAGPDA based on CMS-FOA algorithm is presented. The fusion algorithm gets effective 3-tuple of measurement set by using components of several satisfactory solutions of the fuzzy optimal assignment problem and then uses generalized probabilistic data association algorithm to calculate the update states of targets. Simulation results show that, in the aspect of multi-target tracking accuracy, CMS-FOA algorithm is superior to the optimal assignment (CMS-OA algorithm based on state estimate and CMS-FOAGPDA algorithm is better than CMS-FOA algorithm. But considering the time spent, CMS-FOA algorithm spends a minimum of time and CMS-FOAGPDA algorithm is exactly on the contrary. Therefore, compared with CMS-OA algorithm, the two algorithms presented in the study each has its advantages and should be chosen according to the needs of the actual application when in use.
HYEI: A New Hybrid Evolutionary Imperialist Competitive Algorithm for Fuzzy Knowledge Discovery
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D. Jalal Nouri
2014-01-01
Full Text Available In recent years, imperialist competitive algorithm (ICA, genetic algorithm (GA, and hybrid fuzzy classification systems have been successfully and effectively employed for classification tasks of data mining. Due to overcoming the gaps related to ineffectiveness of current algorithms for analysing high-dimension independent datasets, a new hybrid approach, named HYEI, is presented to discover generic rule-based systems in this paper. This proposed approach consists of three stages and combines an evolutionary-based fuzzy system with two ICA procedures to generate high-quality fuzzy-classification rules. Initially, the best feature subset is selected by using the embedded ICA feature selection, and then these features are used to generate basic fuzzy-classification rules. Finally, all rules are optimized by using an ICA algorithm to reduce their length or to eliminate some of them. The performance of HYEI has been evaluated by using several benchmark datasets from the UCI machine learning repository. The classification accuracy attained by the proposed algorithm has the highest classification accuracy in 6 out of the 7 dataset problems and is comparative to the classification accuracy of the 5 other test problems, as compared to the best results previously published.
Parallelization of Edge Detection Algorithm using MPI on Beowulf Cluster
Haron, Nazleeni; Amir, Ruzaini; Aziz, Izzatdin A.; Jung, Low Tan; Shukri, Siti Rohkmah
In this paper, we present the design of parallel Sobel edge detection algorithm using Foster's methodology. The parallel algorithm is implemented using MPI message passing library and master/slave algorithm. Every processor performs the same sequential algorithm but on different part of the image. Experimental results conducted on Beowulf cluster are presented to demonstrate the performance of the parallel algorithm.
A CLUSTERING ALGORITHM FOR MIXED NUMERIC AND CATEGORICAL DATA
Institute of Scientific and Technical Information of China (English)
Ohn Mar San; Van-Nam Huynh; Yoshiteru Nakamori
2003-01-01
Most of the earlier work on clustering mainly focused on numeric data whose inherent geometric properties can be exploited to naturally define distance functions between data points. However, data mining applications frequently involve many datasets that also consists of mixed numeric and categorical attributes. In this paper we present a clustering algorithm which is based on the k-means algorithm. The algorithm clusters objects with numeric and categorical attributes in a way similar to k-means. The object similarity measure is derived from both numeric and categorical attributes. When applied to numeric data, the algorithm is identical to the k-means. The main result of this paper is to provide a method to update the "cluster centers" of clustering objects described by mixed numeric and categorical attributes in the clustering process to minimize the clustering cost function. The clustering performance of the algorithm is demonstrated with the two well known data sets, namely credit approval and abalone databases.
EFFICIENT ALGORITHM FOR MINING FREQUENT ITEMSETS USING CLUSTERING TECHNIQUES
Directory of Open Access Journals (Sweden)
D.Kerana Hanirex
2011-03-01
Full Text Available Now a days, Association rule plays an important role. The purchasing of one product when another product is purchased represents an association rule. The Apriori algorithm is the basic algorithm for mining association rules. This paper presents an efficient Partition Algorithm for Mining Frequent Itemsets(PAFI using clustering. This algorithm finds the frequent itemsets by partitioning the database transactions into clusters. Clusters are formed based on the imilarity measures between the transactions. Then it finds the frequent itemsets with the transactions in the clusters directly using improved Apriori algorithm which further reduces the number of scans in the database and hence improve the efficiency.
A hybrid monkey search algorithm for clustering analysis.
Chen, Xin; Zhou, Yongquan; Luo, Qifang
2014-01-01
Clustering is a popular data analysis and data mining technique. The k-means clustering algorithm is one of the most commonly used methods. However, it highly depends on the initial solution and is easy to fall into local optimum solution. In view of the disadvantages of the k-means method, this paper proposed a hybrid monkey algorithm based on search operator of artificial bee colony algorithm for clustering analysis and experiment on synthetic and real life datasets to show that the algorithm has a good performance than that of the basic monkey algorithm for clustering analysis.
A Hybrid Monkey Search Algorithm for Clustering Analysis
Directory of Open Access Journals (Sweden)
Xin Chen
2014-01-01
Full Text Available Clustering is a popular data analysis and data mining technique. The k-means clustering algorithm is one of the most commonly used methods. However, it highly depends on the initial solution and is easy to fall into local optimum solution. In view of the disadvantages of the k-means method, this paper proposed a hybrid monkey algorithm based on search operator of artificial bee colony algorithm for clustering analysis and experiment on synthetic and real life datasets to show that the algorithm has a good performance than that of the basic monkey algorithm for clustering analysis.
Directory of Open Access Journals (Sweden)
Vessela Krasteva
Full Text Available This study presents a 2-stage heartbeat classifier of supraventricular (SVB and ventricular (VB beats. Stage 1 makes computationally-efficient classification of SVB-beats, using simple correlation threshold criterion for finding close match with a predominant normal (reference beat template. The non-matched beats are next subjected to measurement of 20 basic features, tracking the beat and reference template morphology and RR-variability for subsequent refined classification in SVB or VB-class by Stage 2. Four linear classifiers are compared: cluster, fuzzy, linear discriminant analysis (LDA and classification tree (CT, all subjected to iterative training for selection of the optimal feature space among extended 210-sized set, embodying interactive second-order effects between 20 independent features. The optimization process minimizes at equal weight the false positives in SVB-class and false negatives in VB-class. The training with European ST-T, AHA, MIT-BIH Supraventricular Arrhythmia databases found the best performance settings of all classification models: Cluster (30 features, Fuzzy (72 features, LDA (142 coefficients, CT (221 decision nodes with top-3 best scored features: normalized current RR-interval, higher/lower frequency content ratio, beat-to-template correlation. Unbiased test-validation with MIT-BIH Arrhythmia database rates the classifiers in descending order of their specificity for SVB-class: CT (99.9%, LDA (99.6%, Cluster (99.5%, Fuzzy (99.4%; sensitivity for ventricular ectopic beats as part from VB-class (commonly reported in published beat-classification studies: CT (96.7%, Fuzzy (94.4%, LDA (94.2%, Cluster (92.4%; positive predictivity: CT (99.2%, Cluster (93.6%, LDA (93.0%, Fuzzy (92.4%. CT has superior accuracy by 0.3-6.8% points, with the advantage for easy model complexity configuration by pruning the tree consisted of easy interpretable 'if-then' rules.
Fuzzy Optimization of an Elevator Mechanism Applying the Genetic Algorithm and Neural Networks
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
Considering the indefinite character of the value of design parameters and being satisfied with load-bearing capacity and stiffness, the fuzzy optimization mathematical model is set up to minimize the volume of tooth corona of a worm gear in an elevator mechanism. The method of second-class comprehensive evaluation was used based on the optimal level cut set, thus the optimal level value of every fuzzy constraint can be attained; the fuzzy optimization is transformed into the usual optimization.The Fast Back Propagation of the neural networks algorithm are adopted to train feed-forward networks so as to fit a relative coefficient. Then the fitness function with penalty terms is built by a penalty strategy, a neural networks program is recalled, and solver functions of the Genetic Algorithm Toolbox of Matlab software are adopted to solve the optimization model.
Bat algorithm optimized fuzzy PD based speed controller for brushless direct current motor
Directory of Open Access Journals (Sweden)
K. Premkumar
2016-06-01
Full Text Available In this paper, design of fuzzy proportional derivative controller and fuzzy proportional derivative integral controller for speed control of brushless direct current drive has been presented. Optimization of the above controllers design is carried out using nature inspired optimization algorithms such as particle swarm, cuckoo search, and bat algorithms. Time domain specifications such as overshoot, undershoot, settling time, recovery time, and steady state error and performance indices such as root mean squared error, integral of absolute error, integral of time multiplied absolute error and integral of squared error are measured and compared for the above controllers under different operating conditions such as varying set speed and load disturbance conditions. The precise investigation through simulation is performed using simulink toolbox. From the simulation test results, it is evident that bat optimized fuzzy proportional derivative controller has superior performance than the other controllers considered. Experimental test results have also been taken and analyzed for the optimal controller identified through simulation.
An on-line algorithm for creating self-organizing fuzzy neural networks.
Leng, Gang; Prasad, Girijesh; McGinnity, Thomas Martin
2004-12-01
This paper presents a new on-line algorithm for creating a self-organizing fuzzy neural network (SOFNN) from sample patterns to implement a singleton or Takagi-Sugeno (TS) type fuzzy model. The SOFNN is based on ellipsoidal basis function (EBF) neurons consisting of a center vector and a width vector. New methods of the structure learning and the parameter learning, based on new adding and pruning techniques and a recursive on-line learning algorithm, are proposed and developed. A proof of the convergence of both the estimation error and the linear network parameters is also given in the paper. The proposed methods are very simple and effective and generate a fuzzy neural model with a high accuracy and compact structure. Simulation work shows that the SOFNN has the capability of self-organization to determine the structure and parameters of the network automatically.
The Georgi algorithms of jet clustering
Ge, Shao-Feng
2015-05-01
We reveal the direct link between the jet clustering algorithms recently proposed by Howard Georgi and parton shower kinematics, providing firm foundation from the theoretical side. The kinematics of this class of elegant algorithms is explored systematically for partons with arbitrary masses and the jet function is generalized to J {/β ( n)} with a jet function index n in order to achieve more degrees of freedom. Based on three basic requirements that, the result of jet clustering is process-independent and hence logically consistent, for softer subjets the inclusion cone is larger to conform with the fact that parton shower tends to emit softer partons at earlier stage with larger opening angle, and that the cone size cannot be too large in order to avoid mixing up neighbor jets, we derive constraints on the jet function parameter β and index n which are closely related to cone size cutoff. Finally, we discuss how jet function values can be made invariant under Lorentz boost.
PROPOSED A HETEROGENEOUS CLUSTERING ALGORITHM TO IMPROVE QOS IN WSN
Directory of Open Access Journals (Sweden)
Mehran Mokhtari
2016-07-01
Full Text Available In this article it has presented leach extended hierarchical 3-level clustered heterogeneous and dynamics algorithm. On suggested protocol (LEH3LA with planning of selected auction cluster head, and alternative cluster head node, problem of delay on processing, processing of selecting members, decrease of expenses, and energy consumption, decrease of sending message, and receiving messages inside the clusters, selecting of cluster heads in large sensor networks were solved. This algorithm uses hierarchical heterogeneous network (3-levels, collective intelligence, and intra-cluster interaction for communications. Also it will solve the problems of sending data in Multi-BS mobile networks, expanding inter-cluster networks, overlap cluster, genesis orphan nodes, boundary change dynamically clusters, using backbone networks, cloud sensor. Using sleep/wake scheduling algorithm or TDMA-schedule alternative cluster head node provides redundancy, and fault tolerance. Local processing in cluster head nodes, and alternative cluster head, intra-cluster and inter-cluster communications such as Multi-HOP cause increase on processing speed, and sending data intra-cluster and inter-cluster. Decrease of overhead network, and increase the load balancing among cluster heads. Using encapsulation of data method, by cluster head nodes, energy consumption decrease during sending data. Also by improving quality of service (QoS in CBRP, LEACH, 802.15.4, decrease of energy consumption in sensors, cluster heads and alternative cluster head nodes, cause increase on lift time of sensor networks
A novel fuzzy C-means algorithm for unsupervised heterogeneous tumor quantification in PET.
Belhassen, Saoussen; Zaidi, Habib
2010-03-01
Accurate and robust image segmentation was identified as one of the most challenging issues facing PET quantification in oncological imaging. This difficulty is compounded by the low spatial resolution and high noise characteristics of PET images. The fuzzy C-means (FCM) clustering algorithm was largely used in various medical image segmentation approaches. However, the algorithm is sensitive to both noise and intensity heterogeneity since it does not take into account spatial contextual information. To overcome this limitation, a new fuzzy segmentation technique adapted to typical noisy and low resolution oncological PET data is proposed. PET images smoothed using a nonlinear anisotropic diffusion filter are added as a second input to the proposed FCM algorithm to incorporate spatial information (FCM-S). In addition, a methodology was developed to integrate the a trous wavelet transform in the standard FCM algorithm (FCM-SW) to allow handling of heterogeneous lesions' uptake. The algorithm was applied to the simulated data of the NCAT phantom, incorporating heterogeneous lesions in the lung and clinical PET/CT images of 21 patients presenting with histologically proven nonsmall-cell lung cancer (NSCLC) and 7 patients presenting with laryngeal squamous cell carcinoma (LSCC) to assess its performance for segmenting tumors with arbitrary size, shape, and tracer uptake. For NSCLC patients, the maximal tumor diameters measured from the macroscopic examination of the surgical specimen served as the ground truth for comparison with the maximum diameter estimated by the segmentation technique, whereas for LSCC patients, the 3D macroscopic tumor volume was considered as the ground truth for comparison with the corresponding PET-based volume. The proposed algorithm was also compared to the classical FCM segmentation technique. There is a good correlation (R2 = 0.942) between the actual maximal diameter of primary NSCLC tumors estimated using the proposed PET segmentation
A New Multivariate Approach for Prognostics Based on Extreme Learning Machine and Fuzzy Clustering.
Javed, Kamran; Gouriveau, Rafael; Zerhouni, Noureddine
2015-12-01
Prognostics is a core process of prognostics and health management (PHM) discipline, that estimates the remaining useful life (RUL) of a degrading machinery to optimize its service delivery potential. However, machinery operates in a dynamic environment and the acquired condition monitoring data are usually noisy and subject to a high level of uncertainty/unpredictability, which complicates prognostics. The complexity further increases, when there is absence of prior knowledge about ground truth (or failure definition). For such issues, data-driven prognostics can be a valuable solution without deep understanding of system physics. This paper contributes a new data-driven prognostics approach namely, an "enhanced multivariate degradation modeling," which enables modeling degrading states of machinery without assuming a homogeneous pattern. In brief, a predictability scheme is introduced to reduce the dimensionality of the data. Following that, the proposed prognostics model is achieved by integrating two new algorithms namely, the summation wavelet-extreme learning machine and subtractive-maximum entropy fuzzy clustering to show evolution of machine degradation by simultaneous predictions and discrete state estimation. The prognostics model is equipped with a dynamic failure threshold assignment procedure to estimate RUL in a realistic manner. To validate the proposition, a case study is performed on turbofan engines data from PHM challenge 2008 (NASA), and results are compared with recent publications.
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Emer Bernal
2017-01-01
Full Text Available In this paper we are presenting a method using fuzzy logic for dynamic parameter adaptation in the imperialist competitive algorithm, which is usually known by its acronym ICA. The ICA algorithm was initially studied in its original form to find out how it works and what parameters have more effect upon its results. Based on this study, several designs of fuzzy systems for dynamic adjustment of the ICA parameters are proposed. The experiments were performed on the basis of solving complex optimization problems, particularly applied to benchmark mathematical functions. A comparison of the original imperialist competitive algorithm and our proposed fuzzy imperialist competitive algorithm was performed. In addition, the fuzzy ICA was compared with another metaheuristic using a statistical test to measure the advantage of the proposed fuzzy approach for dynamic parameter adaptation.
Prediction system of hydroponic plant growth and development using algorithm Fuzzy Mamdani method
Sudana, I. Made; Purnawirawan, Okta; Arief, Ulfa Mediaty
2017-03-01
Hydroponics is a method of farming without soil. One of the Hydroponic plants is Watercress (Nasturtium Officinale). The development and growth process of hydroponic Watercress was influenced by levels of nutrients, acidity and temperature. The independent variables can be used as input variable system to predict the value level of plants growth and development. The prediction system is using Fuzzy Algorithm Mamdani method. This system was built to implement the function of Fuzzy Inference System (Fuzzy Inference System/FIS) as a part of the Fuzzy Logic Toolbox (FLT) by using MATLAB R2007b. FIS is a computing system that works on the principle of fuzzy reasoning which is similar to humans' reasoning. Basically FIS consists of four units which are fuzzification unit, fuzzy logic reasoning unit, base knowledge unit and defuzzification unit. In addition to know the effect of independent variables on the plants growth and development that can be visualized with the function diagram of FIS output surface that is shaped three-dimensional, and statistical tests based on the data from the prediction system using multiple linear regression method, which includes multiple linear regression analysis, T test, F test, the coefficient of determination and donations predictor that are calculated using SPSS (Statistical Product and Service Solutions) software applications.
Health state evaluation of shield tunnel SHM using fuzzy cluster method
Zhou, Fa; Zhang, Wei; Sun, Ke; Shi, Bin
2015-04-01
Shield tunnel SHM is in the path of rapid development currently while massive monitoring data processing and quantitative health grading remain a real challenge, since multiple sensors belonging to different types are employed in SHM system. This paper addressed the fuzzy cluster method based on fuzzy equivalence relationship for the health evaluation of shield tunnel SHM. The method was optimized by exporting the FSV map to automatically generate the threshold value. A new holistic health score(HHS) was proposed and its effectiveness was validated by conducting a pilot test. A case study on Nanjing Yangtze River Tunnel was presented to apply this method. Three types of indicators, namely soil pressure, pore pressure and steel strain, were used to develop the evaluation set U. The clustering results were verified by analyzing the engineering geological conditions; the applicability and validity of the proposed method was also demonstrated. Besides, the advantage of multi-factor evaluation over single-factor model was discussed by using the proposed HHS. This investigation indicated the fuzzy cluster method and HHS is capable of characterizing the fuzziness of tunnel health, and it is beneficial to clarify the tunnel health evaluation uncertainties.
Evaluation of E-Learners Behaviour using Different Fuzzy Clustering Models: A Comparative Study
Hogo, Mofreh A
2010-01-01
This paper introduces an evaluation methodologies for the e-learners' behaviour that will be a feedback to the decision makers in e-learning system. Learner's profile plays a crucial role in the evaluation process to improve the e-learning process performance. The work focuses on the clustering of the e-learners based on their behaviour into specific categories that represent the learner's profiles. The learners' classes named as regular, workers, casual, bad, and absent. The work may answer the question of how to return bad students to be regular ones. The work presented the use of different fuzzy clustering techniques as fuzzy c-means and kernelized fuzzy c-means to find the learners' categories and predict their profiles. The paper presents the main phases as data description, preparation, features selection, and the experiments design using different fuzzy clustering models. Analysis of the obtained results and comparison with the real world behavior of those learners proved that there is a match with per...
A Fuzzy Genetic Algorithm Approach to an Adaptive Information Retrieval Agent.
Martin-Bautista, Maria J.; Vila, Maria-Amparo; Larsen, Henrik Legind
1999-01-01
Presents an approach to a Genetic Information Retrieval Agent Filter (GIRAF) that filters and ranks documents retrieved from the Internet according to users' preferences by using a Genetic Algorithm and fuzzy set theory to handle the imprecision of users' preferences and users' evaluation of the retrieved documents. (Author/LRW)
Genetic algorithm for project time-cost optimization in fuzzy environment
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Khan Md. Ariful Haque
2012-12-01
Full Text Available Purpose: The aim of this research is to develop a more realistic approach to solve project time-cost optimization problem under uncertain conditions, with fuzzy time periods. Design/methodology/approach: Deterministic models for time-cost optimization are never efficient considering various uncertainty factors. To make such problems realistic, triangular fuzzy numbers and the concept of a-cut method in fuzzy logic theory are employed to model the problem. Because of NP-hard nature of the project scheduling problem, Genetic Algorithm (GA has been used as a searching tool. Finally, Dev-C++ 4.9.9.2 has been used to code this solver. Findings: The solution has been performed under different combinations of GA parameters and after result analysis optimum values of those parameters have been found for the best solution. Research limitations/implications: For demonstration of the application of the developed algorithm, a project on new product (Pre-paid electric meter, a project under government finance launching has been chosen as a real case. The algorithm is developed under some assumptions. Practical implications: The proposed model leads decision makers to choose the desired solution under different risk levels. Originality/value: Reports reveal that project optimization problems have never been solved under multiple uncertainty conditions. Here, the function has been optimized using Genetic Algorithm search technique, with varied level of risks and fuzzy time periods.
Fuzzy Tracking and Control Algorithm for an SSVEP-Based BCI System
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Yeou-Jiunn Chen
2016-09-01
Full Text Available Subjects with amyotrophic lateral sclerosis (ALS consistently experience decreasing quality of life because of this distinctive disease. Thus, a practical brain-computer interface (BCI application can effectively help subjects with ALS to participate in communication or entertainment. In this study, a fuzzy tracking and control algorithm is proposed for developing a BCI remote control system. To represent the characteristics of the measured electroencephalography (EEG signals after visual stimulation, a fast Fourier transform is applied to extract the EEG features. A self-developed fuzzy tracking algorithm quickly traces the changes of EEG signals. The accuracy and stability of a BCI system can be greatly improved by using a fuzzy control algorithm. Fifteen subjects were asked to attend a performance test of this BCI system. The canonical correlation analysis (CCA was adopted to compare the proposed approach, and the average recognition rates are 96.97% and 94.49% for proposed approach and CCA, respectively. The experimental results showed that the proposed approach is preferable to CCA. Overall, the proposed fuzzy tracking and control algorithm applied in the BCI system can profoundly help subjects with ALS to control air swimmer drone vehicles for entertainment purposes.
Dynamic Fuzzy Controlled RWA Algorithm for IP/GMPLS over WDM Networks
Institute of Scientific and Technical Information of China (English)
I-Shyan Hwang; I-Feng Huang; Shin-Cheng Yu
2005-01-01
This paper proposes a dynamic RWA scheme using fuzzy logic control on IP/GMPLS over WDM networks to achieve the best quality of network transmission. The proposed algorithm dynamically allocates network resources and reserves partial bandwidth based on the current network status, which includes the request bandwidth, average utilization for each wavelength and its coefficient of variance (C.V.) of data traffic, to determine whether the connection can be set up. Five fuzzy sets for request bandwidth, average rate and C.V. of data traffic are used to divide the variable space: very large (LP), large (SP), normal (ZE), small (SN), and very small (LN). Setting the fuzzy limit is a key part in the proposed algorithm. The simulation of scenarios in this paper has two steps. In the first step, the adaptive fuzzy limits are evaluated based on average transmission cost pertaining to ten network statuses. The second step is to compare the proposed algorithm with periodic measurement of traffic (PMT) in ATM networks in six network situations to show that the proposed FC-RWA algorithm can provide better network transmission.
Genetic Algorithms for Auto-Clustering in KDD
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
In solving the clustering problem in the context of knowledge discovery in databases (KDD), the traditional methods, for example, the K-means algorithm and its variants, usually require the users to provide the number of clusters in advance based on the pro-information. Unfortunately, the number of clusters in general is unknown to the users who are usually short of pro-information. Therefore, the clustering calculation becomes a tedious trial-and-error work, and the result is often not global optimal especially when the number of clusters is large. In this paper, a new dynamic clustering method based on genetic algorithms (GA) is proposed and applied for auto-clustering of data entities in large databases. The algorithm can automatically cluster the data according to their similarities and find the exact number of clusters. Experiment results indicate that the method is of global optimization by dynamically clustering logic.
Energy Aware Clustering Algorithms for Wireless Sensor Networks
Rakhshan, Noushin; Rafsanjani, Marjan Kuchaki; Liu, Chenglian
2011-09-01
The sensor nodes deployed in wireless sensor networks (WSNs) are extremely power constrained, so maximizing the lifetime of the entire networks is mainly considered in the design. In wireless sensor networks, hierarchical network structures have the advantage of providing scalable and energy efficient solutions. In this paper, we investigate different clustering algorithms for WSNs and also compare these clustering algorithms based on metrics such as clustering distribution, cluster's load balancing, Cluster Head's (CH) selection strategy, CH's role rotation, node mobility, clusters overlapping, intra-cluster communications, reliability, security and location awareness.
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Jing Zhao
2013-10-01
Full Text Available The evolutionary learning of fuzzy neural networks (FNN consists of structure learning to determine the proper number of fuzzy rules and parameters learning to adjust the network parameters. Many optimization algorithms can be applied to evolve FNN. However the search space of most algorithms has fixed dimension, which can not suit to dynamic structure learning of FNN. We propose a novel technique, which is named the variable-dimensional quantum-behaved particle swarm optimization algorithm (VDQPSO, to address the problem. In the proposed algorithm, the optimum dimension, which is unknown at the beginning, is updated together with the position of swarm. The optimum dimension converged at the end of the optimization process corresponds to a unique FNN structure where the optimum parameters can be achieved. The results of the prediction of chaotic time series experiment show that the proposed technique is effective. It can evolve to optimum or near-optimum FNN structure and optimum parameters.
Fuzzy-Based Hybrid Control Algorithm for the Stabilization of a Tri-Rotor UAV
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Zain Anwar Ali
2016-05-01
Full Text Available In this paper, a new and novel mathematical fuzzy hybrid scheme is proposed for the stabilization of a tri-rotor unmanned aerial vehicle (UAV. The fuzzy hybrid scheme consists of a fuzzy logic controller, regulation pole-placement tracking (RST controller with model reference adaptive control (MRAC, in which adaptive gains of the RST controller are being fine-tuned by a fuzzy logic controller. Brushless direct current (BLDC motors are installed in the triangular frame of the tri-rotor UAV, which helps maintain control on its motion and different altitude and attitude changes, similar to rotorcrafts. MRAC-based MIT rule is proposed for system stability. Moreover, the proposed hybrid controller with nonlinear flight dynamics is shown in the presence of translational and rotational velocity components. The performance of the proposed algorithm is demonstrated via MATLAB simulations, in which the proposed fuzzy hybrid controller is compared with the existing adaptive RST controller. It shows that our proposed algorithm has better transient performance with zero steady-state error, and fast convergence towards stability.
Fuzzy-Based Hybrid Control Algorithm for the Stabilization of a Tri-Rotor UAV.
Ali, Zain Anwar; Wang, Daobo; Aamir, Muhammad
2016-05-09
In this paper, a new and novel mathematical fuzzy hybrid scheme is proposed for the stabilization of a tri-rotor unmanned aerial vehicle (UAV). The fuzzy hybrid scheme consists of a fuzzy logic controller, regulation pole-placement tracking (RST) controller with model reference adaptive control (MRAC), in which adaptive gains of the RST controller are being fine-tuned by a fuzzy logic controller. Brushless direct current (BLDC) motors are installed in the triangular frame of the tri-rotor UAV, which helps maintain control on its motion and different altitude and attitude changes, similar to rotorcrafts. MRAC-based MIT rule is proposed for system stability. Moreover, the proposed hybrid controller with nonlinear flight dynamics is shown in the presence of translational and rotational velocity components. The performance of the proposed algorithm is demonstrated via MATLAB simulations, in which the proposed fuzzy hybrid controller is compared with the existing adaptive RST controller. It shows that our proposed algorithm has better transient performance with zero steady-state error, and fast convergence towards stability.
Fuzzy-Based Hybrid Control Algorithm for the Stabilization of a Tri-Rotor UAV
Ali, Zain Anwar; Wang, Daobo; Aamir, Muhammad
2016-01-01
In this paper, a new and novel mathematical fuzzy hybrid scheme is proposed for the stabilization of a tri-rotor unmanned aerial vehicle (UAV). The fuzzy hybrid scheme consists of a fuzzy logic controller, regulation pole-placement tracking (RST) controller with model reference adaptive control (MRAC), in which adaptive gains of the RST controller are being fine-tuned by a fuzzy logic controller. Brushless direct current (BLDC) motors are installed in the triangular frame of the tri-rotor UAV, which helps maintain control on its motion and different altitude and attitude changes, similar to rotorcrafts. MRAC-based MIT rule is proposed for system stability. Moreover, the proposed hybrid controller with nonlinear flight dynamics is shown in the presence of translational and rotational velocity components. The performance of the proposed algorithm is demonstrated via MATLAB simulations, in which the proposed fuzzy hybrid controller is compared with the existing adaptive RST controller. It shows that our proposed algorithm has better transient performance with zero steady-state error, and fast convergence towards stability. PMID:27171084
Bruin, de S.; Stein, A.
1998-01-01
This study explores the use of fuzzy c-means clustering of attribute data derived from a digital elevation model to represent transition zones in the soil-landscape. The conventional geographic model used for soil-landscape description is not able to properly deal with these. Fuzzy c-means clusterin
Fuzzy C-Means Clustering Model Data Mining For Recognizing Stock Data Sampling Pattern
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Sylvia Jane Annatje Sumarauw
2007-06-01
Full Text Available Abstract Capital market has been beneficial to companies and investor. For investors, the capital market provides two economical advantages, namely deviden and capital gain, and a non-economical one that is a voting .} hare in Shareholders General Meeting. But, it can also penalize the share owners. In order to prevent them from the risk, the investors should predict the prospect of their companies. As a consequence of having an abstract commodity, the share quality will be determined by the validity of their company profile information. Any information of stock value fluctuation from Jakarta Stock Exchange can be a useful consideration and a good measurement for data analysis. In the context of preventing the shareholders from the risk, this research focuses on stock data sample category or stock data sample pattern by using Fuzzy c-Me, MS Clustering Model which providing any useful information jar the investors. lite research analyses stock data such as Individual Index, Volume and Amount on Property and Real Estate Emitter Group at Jakarta Stock Exchange from January 1 till December 31 of 204. 'he mining process follows Cross Industry Standard Process model for Data Mining (CRISP,. DM in the form of circle with these steps: Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation and Deployment. At this modelling process, the Fuzzy c-Means Clustering Model will be applied. Data Mining Fuzzy c-Means Clustering Model can analyze stock data in a big database with many complex variables especially for finding the data sample pattern, and then building Fuzzy Inference System for stimulating inputs to be outputs that based on Fuzzy Logic by recognising the pattern. Keywords: Data Mining, AUz..:y c-Means Clustering Model, Pattern Recognition
A Novel Clustering Algorithm Inspired by Membrane Computing
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Hong Peng
2015-01-01
Full Text Available P systems are a class of distributed parallel computing models; this paper presents a novel clustering algorithm, which is inspired from mechanism of a tissue-like P system with a loop structure of cells, called membrane clustering algorithm. The objects of the cells express the candidate centers of clusters and are evolved by the evolution rules. Based on the loop membrane structure, the communication rules realize a local neighborhood topology, which helps the coevolution of the objects and improves the diversity of objects in the system. The tissue-like P system can effectively search for the optimal partitioning with the help of its parallel computing advantage. The proposed clustering algorithm is evaluated on four artificial data sets and six real-life data sets. Experimental results show that the proposed clustering algorithm is superior or competitive to k-means algorithm and several evolutionary clustering algorithms recently reported in the literature.
Optimal fuzzy PID control tuned with genetic algorithms
Santos, Carlos Miguel Almeida
2013-01-01
Fuzzy logic controllers (FLC) are intelligent systems, based on heuristic knowledge, that have been largely applied in numerous areas of everyday life. They can be used to describe a linear or nonlinear system and are suitable when a real system is not known or too difficult to find their model. FLC provide a formal methodology for representing, manipulating and implementing a human heuristic knowledge on how to control a system. These controllers can be seen as artificial decision makers tha...
Intelligent micro blood typing system using a fuzzy algorithm
Kang, Taeyun; Lee, Seung-Jae; Kim, Yonggoo; Lee, Gyoo-Whung; Cho, Dong-Woo
2010-01-01
ABO typing is the first analysis performed on blood when it is tested for transfusion purposes. The automated machines used in hospitals for this purpose are typically very large and the process is complicated. In this paper, we present a new micro blood typing system that is an improved version of our previous system (Kang et al 2004 Trans. ASME, J. Manuf. Sci. Eng. 126 766, Lee et al 2005 Sensors Mater. 17 113). This system, fabricated using microstereolithography, has a passive valve for controlling the flow of blood and antibodies. The intelligent micro blood typing system has two parts: a single-line micro blood typing device and a fuzzy expert system for grading the strength of agglutination. The passive valve in the single-line micro blood typing device makes the blood stop at the entrance of a micro mixer and lets it flow again after the blood encounters antibodies. Blood and antibodies are mixed in the micro mixer and agglutination occurs in the chamber. The fuzzy expert system then determines the degree of agglutination from images of the agglutinated blood. Blood typing experiments using this device were successful, and the fuzzy expert system produces a grading decision comparable to that produced by an expert conducting a manual analysis.
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Endra Joelianto
2009-11-01
Full Text Available The well known PID controller has inherent limitations in fulfilling simultaneously the conflicting control design objectives. Parameters of the tuned PID controller should trade off the requirement of tracking set-point performances, disturbance rejection and stability robustness. Combination of hybrid reference control (HRC with PID controller results in the transient response performances can be independently achieved without deteriorating the disturbance rejection properties and the stability robustness requirement. This paper proposes a fuzzy based HRC where the membership functions of the fuzzy logic system are obtained by using a substractive clustering technique. The proposed method guarantees the transient response performances satisfaction while preserving the stability robustness of the closed loop system controlled by the PID controller with effective and systematic procedures in designing the fuzzy hybrid reference control system.
HYBRID OF FUZZY CLUSTERING NEURAL NETWORK OVER NSL DATASET FOR INTRUSION DETECTION SYSTEM
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Dahlia Asyiqin Ahmad Zainaddin
2013-01-01
Full Text Available Intrusion Detection System (IDS is one of the component that take part in the system defence, to identify abnormal activities happening in the computer system. Nowadays, IDS facing composite demands to defeat modern attack activities from damaging the computer systems. Anomaly-Based IDS examines ongoing traffic, activity, transactions and behavior in order to identify intrusions by detecting anomalies. These technique identifies activities which degenerates from the normal behaviours. In recent years, data mining approach for intrusion detection have been advised and used. The approach such as Genetic Algorithms , Support Vector Machines, Neural Networks as well as clustering has resulted in high accuracy and good detection rates but with moderate false alarm on novel attacks. Many researchers also have proposed hybrid data mining techniques. The previous resechers has intoduced the combination of Fuzzy Clustering and Artificial Neural Network. However, it was tested only on randomn selection of KDDCup 1999 dataset. In this study the framework experiment introduced, has been used over the NSL dataset to test the stability and reliability of the technique. The result of precision, recall and f-value rate is compared with previous experiment. Both dataset covers four types of main attacks, which are Derial of Services (DoS, User to Root (U2R, Remote to Local (R2L and Probe. Results had guarenteed that the hybrid approach performed better detection especially for low frequent over NSL datataset compared to original KDD dataset, due to the removal of redundancy and uncomplete elements in the original dataset. This electronic document is a âliveâ template. The various components of your paper [title, text, tables, figures and references] are already defined on the style sheet, as illustrated by the portions given in this document.
An energy efficient clustering routing algorithm for wireless sensor networks
Institute of Scientific and Technical Information of China (English)
LI Li; DONG Shu-song; WEN Xiang-ming
2006-01-01
This article proposes an energy efficient clustering routing (EECR) algorithm for wireless sensor network. The algorithm can divide a sensor network into a few clusters and select a cluster head base on weight value that leads to more uniform energy dissipation evenly among all sensor nodes.Simulations and results show that the algorithm can save overall energy consumption and extend the lifetime of the wireless sensor network.
PENGGUNAAN HIBRIDISASI GENETICS ALGORITHMS DAN FUZZY SETS UNTUK MEMPRODUKSI PAKET SOAL
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Rolly Intan
2005-01-01
Full Text Available At least, two important factors, discrimination and difficulty, should be considered in determing whether a problem should be in a packet of problems produced for students entrance examination at a university. The higher the discrimination degree of a problem, the better the problem is used to make a selection of participants based on their intellectual capability. How to provide a packet of entrance examination problems satisfying a determined pattern of discrimination and difficulty is a major problem in this paper for which an algorithm, it can be proved that the beneficiary of applying fuzzy sets and fuzzy relation in determing the first chromosome in the process of GA is that the process can reach tolerable solutions faster. Maximum number of generation is still needed as a threshold to overcome the problem of run time system overflow. Generally, the problems in the form of passages tend to have lower fitnest cost. Abstract in Bahasa Indonesia : Proses penyusunan paket soal (misalnya soal untuk test seleksi masuk universitas yang diambil dari suatu bank soal, minimal harus memperhatikan dua aspek penting yaitu: tingkat kesulitan dan tingkat diskriminan soal. Semakin tinggi tingkat diskriminan suatu soal, semakin baik soal tersebut dipakai untuk menyeleksi kemampuan peserta test. Permasalahan yang dihadapi adalah bagaimana agar pembuat soal dapat memilih dan menentukan kombinasi soal-soal yang tepat (optimum sehingga dapat memenuhi tingkat kesulitan dan diskriminan yang dikehendaki. Untuk menyelesaikan masalah ini, diperkenalkan suatu algoritma yang disusun dengan menggunakan hibridisasi metode Genetics Algorithm dan fuzzy sets. Dari hasil pengujian, didapatkan bahwa penggunaan fuzzy sets dan fuzzy relations dalam pemilihan kromoson awal akan lebih mempercepat pencapaian tolerable solutions. Tetap dibutuhkan treshould maksimum jumlah generasi yang dilakukan untuk mencegah run time sistem overflow. Soal bacaan cenderung memiliki nilai Fitnest
Introduction to Clustering Algorithms and Applications
Yang, Sibei; Tao, Liangde; Gong, Bingchen
2014-01-01
Data clustering is the process of identifying natural groupings or clusters within multidimensional data based on some similarity measure. Clustering is a fundamental process in many different disciplines. Hence, researchers from different fields are actively working on the clustering problem. This paper provides an overview of the different representative clustering methods. In addition, application of clustering in different field is briefly introduced.
集群资源模糊聚类划分模型%Fuzzy Clustering Partition Model of Cluster Resource
Institute of Scientific and Technical Information of China (English)
那丽春
2012-01-01
A fuzzy clustering partition model of cluster resource is proposed in this paper. It quantizes and normalizes the computer resource parameters of CPU, memory, I/O, network adapter and net. It uses fuzzy clustering technique to realize the partition of the computing nodes in the computer clusters. By using of the vector of resource demand and the vector of lowest inaccuracy tolerance, it can divide the computer cluster into several classes and the performance of these computers in one class is more similar. Test results show that this model can effectively partition the computer cluster and it fits the resource schedule of cloud computing.%提出一种集群资源模糊聚类划分模型.对计算机集群中计算节点的CPU、内存、网络、I/O和网卡资源参数进行量化和规范化,运用模糊聚类技术,实现计算节点的聚类划分.引入任务资源需求向量和最低误差容忍向量,将计算机集群划分为若干个性能均衡的逻辑子群.测试结果表明,该模型能有效划分计算机集群,适用于云计算领域的资源调度.
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Woonki Na
2017-03-01
Full Text Available This paper presents an improved maximum power point tracking (MPPT algorithm using a fuzzy logic controller (FLC in order to extract potential maximum power from photovoltaic cells. The objectives of the proposed algorithm are to improve the tracking speed, and to simultaneously solve the inherent drawbacks such as slow tracking in the conventional perturb and observe (P and O algorithm. The performances of the conventional P and O algorithm and the proposed algorithm are compared by using MATLAB/Simulink in terms of the tracking speed and steady-state oscillations. Additionally, both algorithms were experimentally validated through a digital signal processor (DSP-based controlled-boost DC-DC converter. The experimental results show that the proposed algorithm performs with a shorter tracking time, smaller output power oscillation, and higher efficiency, compared with the conventional P and O algorithm.
PHC: A Fast Partition and Hierarchy-Based Clustering Algorithm
Institute of Scientific and Technical Information of China (English)
ZHOU HaoFeng(周皓峰); YUAN QingQing(袁晴晴); CHENG ZunPing(程尊平); SHI BaiLe(施伯乐)
2003-01-01
Cluster analysis is a process to classify data in a specified data set. In this field,much attention is paid to high-efficiency clustering algorithms. In this paper, the features in thecurrent partition-based and hierarchy-based algorithms are reviewed, and a new hierarchy-basedalgorithm PHC is proposed by combining advantages of both algorithms, which uses the cohesionand the closeness to amalgamate the clusters. Compared with similar algorithms, the performanceof PHC is improved, and the quality of clustering is guaranteed. And both the features were provedby the theoretic and experimental analyses in the paper.
Counterexamples to convergence theorem of maximum-entropy clustering algorithm
Institute of Scientific and Technical Information of China (English)
于剑; 石洪波; 黄厚宽; 孙喜晨; 程乾生
2003-01-01
In this paper, we surveyed the development of maximum-entropy clustering algorithm, pointed out that the maximum-entropy clustering algorithm is not new in essence, and constructed two examples to show that the iterative sequence given by the maximum-entropy clustering algorithm may not converge to a local minimum of its objective function, but a saddle point. Based on these results, our paper shows that the convergence theorem of maximum-entropy clustering algorithm put forward by Kenneth Rose et al. does not hold in general cases.
Energy Technology Data Exchange (ETDEWEB)
Tsantis, Stavros [Department of Medical Physics, School of Medicine, University of Patras, Rion, GR 26504 (Greece); Spiliopoulos, Stavros; Karnabatidis, Dimitrios [Department of Radiology, School of Medicine, University of Patras, Rion, GR 26504 (Greece); Skouroliakou, Aikaterini [Department of Energy Technology Engineering, Technological Education Institute of Athens, Athens 12210 (Greece); Hazle, John D. [Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 (United States); Kagadis, George C., E-mail: gkagad@gmail.com, E-mail: George.Kagadis@med.upatras.gr, E-mail: GKagadis@mdanderson.org [Department of Medical Physics, School of Medicine, University of Patras, Rion, GR 26504, Greece and Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030 (United States)
2014-07-15
Purpose: Speckle suppression in ultrasound (US) images of various anatomic structures via a novel speckle noise reduction algorithm. Methods: The proposed algorithm employs an enhanced fuzzy c-means (EFCM) clustering and multiresolution wavelet analysis to distinguish edges from speckle noise in US images. The edge detection procedure involves a coarse-to-fine strategy with spatial and interscale constraints so as to classify wavelet local maxima distribution at different frequency bands. As an outcome, an edge map across scales is derived whereas the wavelet coefficients that correspond to speckle are suppressed in the inverse wavelet transform acquiring the denoised US image. Results: A total of 34 thyroid, liver, and breast US examinations were performed on a Logiq 9 US system. Each of these images was subjected to the proposed EFCM algorithm and, for comparison, to commercial speckle reduction imaging (SRI) software and another well-known denoising approach, Pizurica's method. The quantification of the speckle suppression performance in the selected set of US images was carried out via Speckle Suppression Index (SSI) with results of 0.61, 0.71, and 0.73 for EFCM, SRI, and Pizurica's methods, respectively. Peak signal-to-noise ratios of 35.12, 33.95, and 29.78 and edge preservation indices of 0.94, 0.93, and 0.86 were found for the EFCM, SIR, and Pizurica's method, respectively, demonstrating that the proposed method achieves superior speckle reduction performance and edge preservation properties. Based on two independent radiologists’ qualitative evaluation the proposed method significantly improved image characteristics over standard baseline B mode images, and those processed with the Pizurica's method. Furthermore, it yielded results similar to those for SRI for breast and thyroid images significantly better results than SRI for liver imaging, thus improving diagnostic accuracy in both superficial and in-depth structures. Conclusions: A
2-D minimum fuzzy entropy method of image thresholding based on genetic algorithm
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
A new image thresholding method is introduced, which is based on 2-D histgram and minimizing the measures of fuzziness of an input image. A new definition of fuzzy membership function is proposed, it denotes the characteristic relationship between the gray level of each pixel and the average value of its neighborhood. When the threshold is not located at the obvious and deep valley ofthe histgram, genetic algorithm is devoted to the problem of selecting the appropriate threshold value. The experimental results indicate that the proposed method has good performance.
Energy Technology Data Exchange (ETDEWEB)
Javaheri, Zahra
2010-09-15
Modeling, evaluating and analyzing performance of Iranian thermal power plants is the main goal of this study which is based on multi variant methods analysis. These methods include fuzzy DEA and adaptive neural network algorithm. At first, we determine indicators, then data is collected, next we obtained values of ranking and efficiency by Fuzzy DEA, Case study is thermal power plants In view of the fact that investment to establish on power plant is very high, and maintenance of power plant causes an expensive expenditure, moreover using fossil fuel effected environment hence optimum produce of current power plants is important.
Energy Technology Data Exchange (ETDEWEB)
Larbes, C.; Ait Cheikh, S.M.; Obeidi, T.; Zerguerras, A. [Laboratoire des Dispositifs de Communication et de Conversion Photovoltaique, Departement d' Electronique, Ecole Nationale Polytechnique, 10, Avenue Hassen Badi, El Harrach, Alger 16200 (Algeria)
2009-10-15
This paper presents an intelligent control method for the maximum power point tracking (MPPT) of a photovoltaic system under variable temperature and irradiance conditions. First, for the purpose of comparison and because of its proven and good performances, the perturbation and observation (P and O) technique is briefly introduced. A fuzzy logic controller based MPPT (FLC) is then proposed which has shown better performances compared to the P and O MPPT based approach. The proposed FLC has been also improved using genetic algorithms (GA) for optimisation. Different development stages are presented and the optimized fuzzy logic MPPT controller (OFLC) is then simulated and evaluated, which has shown better performances. (author)
Genetic algorithm-fuzzy based dynamic motion planning approach for a mobile robot
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Presents the mobile robots dynamic motion planning problem with a task to find an obstacle-free route that requires minimum travel time from the start point to the destination point in a changing environment, due to the obstacle's moving. An Genetic Algorithm fuzzy(GA-Fuzzy)based optimal approach proposed to find any obstacle-free path and the GA used to select the optimal one, points ont that using this learned knowledge off line, a mobile robot can navigate to its goal point when it faces new scenario on-line. Concludes with the opti mal rule base given and the simulation results showing its effectiveness.
A Fuzzy Search Algorithm for Structured P2P Network Based on Multi-dimensional Semantic Matrix
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Chuiwei Lu
2012-02-01
Full Text Available Structured P2P network is highly efficient and low cost in resources search, but it only supports single-keyword precise search rather than multi-keyword fuzzy search. This paper puts forward a novel search algorithm FSA-MDSM based on semantic vector matrix, in which the P2P node resources can be transferred into a number of semantic vectors which are formed into a vector matrix in terms of a free semantic dictionary. And according to the similarity of vectors semantics, the vectors are classified into several sub-blocks, each of which is managed by a virtual node. Those virtual nodes with similar semantics will cluster into several special subnets by the way of exchange of the semantic information, and a semantics routing table will be generated. The table can guide a node to locate target resource rapidly. The simulation experiments demonstrate that by using FSA-MDSM algorithm, a fuzzy semantic search can be supported efficiently in the Structured P2P network and its original advantage can not be lost basically
An Incremental Algorithm of Text Clustering Based on Semantic Sequences
Institute of Scientific and Technical Information of China (English)
FENG Zhonghui; SHEN Junyi; BAO Junpeng
2006-01-01
This paper proposed an incremental textclustering algorithm based on semantic sequence.Using similarity relation of semantic sequences and calculating the cover of similarity semantic sequences set, the candidate cluster with minimum entropy overlap value was selected as a result cluster every time in this algorithm.The comparison of experimental results shows that the precision of the algorithm is higher than other algorithms under same conditions and this is obvious especially on long documents set.
Hybrid Ant Bee Algorithm for Fuzzy Expert System Based Sample Classification.
GaneshKumar, Pugalendhi; Rani, Chellasamy; Devaraj, Durairaj; Victoire, T Aruldoss Albert
2014-01-01
Accuracy maximization and complexity minimization are the two main goals of a fuzzy expert system based microarray data classification. Our previous Genetic Swarm Algorithm (GSA) approach has improved the classification accuracy of the fuzzy expert system at the cost of their interpretability. The if-then rules produced by the GSA are lengthy and complex which is difficult for the physician to understand. To address this interpretability-accuracy tradeoff, the rule set is represented using integer numbers and the task of rule generation is treated as a combinatorial optimization task. Ant colony optimization (ACO) with local and global pheromone updations are applied to find out the fuzzy partition based on the gene expression values for generating simpler rule set. In order to address the formless and continuous expression values of a gene, this paper employs artificial bee colony (ABC) algorithm to evolve the points of membership function. Mutual Information is used for idenfication of informative genes. The performance of the proposed hybrid Ant Bee Algorithm (ABA) is evaluated using six gene expression data sets. From the simulation study, it is found that the proposed approach generated an accurate fuzzy system with highly interpretable and compact rules for all the data sets when compared with other approaches.
Classifying OECD Countries According to Health Indicators Using Fuzzy Clustering Ana lysis
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Nesrin Alptekin
2015-12-01
Full Text Available This study was conducted in order to classify OECD countries according to health indicators using fuzzy clustering analysis, to identify the cluster in which Turkey is in and the other countries located in the same cluster with Turkey and to determine whether Turkey shows similar characteristics with other countries located in the same cluster or not. In the study, 34 OECD member countries were discussed. With ten variables that directly and indirectly affect the health, c- means clustering analysis was performed. The NCSS 10 software package was used to analyze the data.In the analysis, it was determined that the most appropriate cluster number is five; three countries involved in the first cluster, nine countries involved in the second cluster, nine countries involved in the third cluster, six countries involved in the fourth cluster and seven countries involved in the fifth cluster. Turkey is located in the fourth cluster. Other countries in the same cluster along with Turkey are Estonia, Hungary, Mexico, Poland and Chile
A new efficient Cluster Algorithm for the Ising Model
Nyffeler, M; Wiese, U J; Nyfeler, Matthias; Pepe, Michele; Wiese, Uwe-Jens
2005-01-01
Using D-theory we construct a new efficient cluster algorithm for the Ising model. The construction is very different from the standard Swendsen-Wang algorithm and related to worm algorithms. With the new algorithm we have measured the correlation function with high precision over a surprisingly large number of orders of magnitude.
Application of Bibliographic Coupling versus Cited Titles Words in Patent Fuzzy Clustering
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Anahita Kermani
2013-03-01
Full Text Available Attribute selection is one of the steps before patent clustering. Various attributes can be used for clustering. In this study, the effect of using citation and citation title words, respectively, in form of bibliographic coupling and citation title words sharing, were measured and compared with each other, as patent attributes. This study was done in an experimental method, on a collection of 717 US Patent cited in the patents belong to 977/774 subclass of US Patent Classification. Fuzzy C-means was used for patent clustering and extended BCubed precision and extended BCubed recall were used as evaluation measure. The results showed that the clustering produced by bibliographic coupling had better performance than clustering used citation title words and existence of cluster structure were in a wider range of exhaustivity than citation title words.
Huang, Wei; Oh, Sung-Kwun; Pedrycz, Witold
2014-12-01
In this study, we propose Hybrid Radial Basis Function Neural Networks (HRBFNNs) realized with the aid of fuzzy clustering method (Fuzzy C-Means, FCM) and polynomial neural networks. Fuzzy clustering used to form information granulation is employed to overcome a possible curse of dimensionality, while the polynomial neural network is utilized to build local models. Furthermore, genetic algorithm (GA) is exploited here to optimize the essential design parameters of the model (including fuzzification coefficient, the number of input polynomial fuzzy neurons (PFNs), and a collection of the specific subset of input PFNs) of the network. To reduce dimensionality of the input space, principal component analysis (PCA) is considered as a sound preprocessing vehicle. The performance of the HRBFNNs is quantified through a series of experiments, in which we use several modeling benchmarks of different levels of complexity (different number of input variables and the number of available data). A comparative analysis reveals that the proposed HRBFNNs exhibit higher accuracy in comparison to the accuracy produced by some models reported previously in the literature.
URL Mining Using Agglomerative Clustering Algorithm
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Chinmay R. Deshmukh
2015-02-01
Full Text Available Abstract The tremendous growth of the web world incorporates application of data mining techniques to the web logs. Data Mining and World Wide Web encompasses an important and active area of research. Web log mining is analysis of web log files with web pages sequences. Web mining is broadly classified as web content mining web usage mining and web structure mining. Web usage mining is a technique to discover usage patterns from Web data in order to understand and better serve the needs of Web-based applications. URL mining refers to a subclass of Web mining that helps us to investigate the details of a Uniform Resource Locator. URL mining can be advantageous in the fields of security and protection. The paper introduces a technique for mining a collection of user transactions with an Internet search engine to discover clusters of similar queries and similar URLs. The information we exploit is a clickthrough data each record consist of a users query to a search engine along with the URL which the user selected from among the candidates offered by search engine. By viewing this dataset as a bipartite graph with the vertices on one side corresponding to queries and on the other side to URLs one can apply an agglomerative clustering algorithm to the graphs vertices to identify related queries and URLs.
A fingerprint identification algorithm by clustering similarity
Institute of Scientific and Technical Information of China (English)
TIAN Jie; HE Yuliang; CHEN Hong; YANG Xin
2005-01-01
This paper introduces a fingerprint identification algorithm by clustering similarity with the view to overcome the dilemmas encountered in fingerprint identification.To decrease multi-spectrum noises in a fingerprint, we first use a dyadic scale space (DSS) method for image enhancement. The second step describes the relative features among minutiae by building a minutia-simplex which contains a pair of minutiae and their local associated ridge information, with its transformation-variant and invariant relative features applied for comprehensive similarity measurement and for parameter estimation respectively. The clustering method is employed to estimate the transformation space.Finally, multi-resolution technique is used to find an optimal transformation model for getting the maximal mutual information between the input and the template features. The experimental results including the performance evaluation by the 2nd International Verification Competition in 2002 (FVC2002), over the four fingerprint databases of FVC2002 indicate that our method is promising in an automatic fingerprint identification system (AFIS).
Directory of Open Access Journals (Sweden)
Haitao Zhang
2015-01-01
Full Text Available We first analyze the effect of network-induced delay on the stability of networked control systems (NCSs. Then, aiming at stochastic characteristics of the time delay, we introduce a new Smith predictor to remove the exponential function with the time delay in the closed-loop characteristic equation of the NCS. Furthermore, we combine the fuzzy PID algorithm with the fuzzy immune control algorithm and present a fuzzy immune self-adaptive PID algorithm to compensate the influence of the model deviation of the controlled object. At last, a kind of fuzzy immune self-adaptive PID algorithm based on new Smith predictor is presented to apply to the NCS. The simulation research on a DC motor is given to show the effectiveness of the proposed algorithm.
Energy Technology Data Exchange (ETDEWEB)
Lee, E.T.
1983-01-01
Algorithms for the construction of the Chomsky and Greibach normal forms for a fuzzy context-free grammar using the algebraic approach are presented and illustrated by examples. The results obtained in this paper may have useful applications in fuzzy languages, pattern recognition, information storage and retrieval, artificial intelligence, database and pictorial information systems. 16 references.
Application of hybrid clustering using parallel k-means algorithm and DIANA algorithm
Umam, Khoirul; Bustamam, Alhadi; Lestari, Dian
2017-03-01
DNA is one of the carrier of genetic information of living organisms. Encoding, sequencing, and clustering DNA sequences has become the key jobs and routine in the world of molecular biology, in particular on bioinformatics application. There are two type of clustering, hierarchical clustering and partitioning clustering. In this paper, we combined two type clustering i.e. K-Means (partitioning clustering) and DIANA (hierarchical clustering), therefore it called Hybrid clustering. Application of hybrid clustering using Parallel K-Means algorithm and DIANA algorithm used to clustering DNA sequences of Human Papillomavirus (HPV). The clustering process is started with Collecting DNA sequences of HPV are obtained from NCBI (National Centre for Biotechnology Information), then performing characteristics extraction of DNA sequences. The characteristics extraction result is store in a matrix form, then normalize this matrix using Min-Max normalization and calculate genetic distance using Euclidian Distance. Furthermore, the hybrid clustering is applied by using implementation of Parallel K-Means algorithm and DIANA algorithm. The aim of using Hybrid Clustering is to obtain better clusters result. For validating the resulted clusters, to get optimum number of clusters, we use Davies-Bouldin Index (DBI). In this study, the result of implementation of Parallel K-Means clustering is data clustered become 5 clusters with minimal IDB value is 0.8741, and Hybrid Clustering clustered data become 13 sub-clusters with minimal IDB values = 0.8216, 0.6845, 0.3331, 0.1994 and 0.3952. The IDB value of hybrid clustering less than IBD value of Parallel K-Means clustering only that perform at 1ts stage. Its means clustering using Hybrid Clustering have the better result to clustered DNA sequence of HPV than perform parallel K-Means Clustering only.
Wu, Xiao; Shen, Jiong; Li, Yiguo; Lee, Kwang Y
2014-05-01
This paper develops a novel data-driven fuzzy modeling strategy and predictive controller for boiler-turbine unit using fuzzy clustering and subspace identification (SID) methods. To deal with the nonlinear behavior of boiler-turbine unit, fuzzy clustering is used to provide an appropriate division of the operation region and develop the structure of the fuzzy model. Then by combining the input data with the corresponding fuzzy membership functions, the SID method is extended to extract the local state-space model parameters. Owing to the advantages of the both methods, the resulting fuzzy model can represent the boiler-turbine unit very closely, and a fuzzy model predictive controller is designed based on this model. As an alternative approach, a direct data-driven fuzzy predictive control is also developed following the same clustering and subspace methods, where intermediate subspace matrices developed during the identification procedure are utilized directly as the predictor. Simulation results show the advantages and effectiveness of the proposed approach.
An Adaptive Fuzzy Clustering and Location Management in Mobile Ad Hoc Networks
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Obulla Reddy
2012-11-01
Full Text Available In the typical Ad Hoc networks application, the network hosts usually perform the given task according to groups, e.g. the command and control over staff and accruement in military affairs, traffic management, etc. Therefore, it is very significant for the study of multicast routing protocols of the Ad Hoc networks. Multicast protocols in MANETs must consider control overhead for maintenance, energy efficiency of nodes and routing trees managements to frequent changes of network topology. Now-a days Multicast protocols extended with Cluster based approach. Cluster based multicast tree formation is still research issues. The mobility of nodes will always increase the communication delay because of re-clustering and cluster head selections. For this issue we evaluate Adaptive Fuzzy System (AFS to multicast communication in mobile ad hoc networks (MANETs. To evaluate the performance of AFS, we simulate the fuzzy clustering in a variety of mobile network topologies in NS-2 and compare it with Cluster-based On Demand Multicast Routing Protocol (CODMRP and Cluster-based routing protocol (CBRP. Our simulation result shows the effectiveness and efficiency of AFMR: high packet delivery ratio is achieved while the delay and overhead are the lowest.
Local Community Detection Algorithm Based on Minimal Cluster
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Yong Zhou
2016-01-01
Full Text Available In order to discover the structure of local community more effectively, this paper puts forward a new local community detection algorithm based on minimal cluster. Most of the local community detection algorithms begin from one node. The agglomeration ability of a single node must be less than multiple nodes, so the beginning of the community extension of the algorithm in this paper is no longer from the initial node only but from a node cluster containing this initial node and nodes in the cluster are relatively densely connected with each other. The algorithm mainly includes two phases. First it detects the minimal cluster and then finds the local community extended from the minimal cluster. Experimental results show that the quality of the local community detected by our algorithm is much better than other algorithms no matter in real networks or in simulated networks.
A Load Balance Routing Algorithm Based on Uneven Clustering
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Liang Yuan
2013-10-01
Full Text Available Aiming at the problem of uneven load in clustering Wireless Sensor Network (WSN, a kind of load balance routing algorithm based on uneven clustering is proposed to do uneven clustering and calculate optimal number of clustering. This algorithm prevents the number of common node under some certain cluster head from being too large which leads load to be overweight to death through even node clustering. It constructs evaluation function which can better reflect residual energy distribution of nodes and at the same time constructs routing evaluation function between cluster heads which uses MATLAB to do simulation on the performance of this algorithm. Simulation result shows that the routing established by this algorithm effectively improves network’s energy balance and lengthens the life cycle of network.
A novel vehicle navigation map matching algorithm based on fuzzy logic and its application
Institute of Scientific and Technical Information of China (English)
TONG Xiao-hua; WU Song-chun; WU Shu-qing; LIU Da-jie
2005-01-01
A new real-time map matching algorithm based on fuzzy logic is proposed. 3 main factors affecting the reliability of map matching, including the distance between the vehicle location and the matching road segment, the angle between the vehicle direction and the road segment direction and the road connectivity are discussed. Fuzzy rules for the distance, angle and connectivity are presented to calculate the matching reliability. 2 indicators for estimating the matching reliability are then derived, one is the lower limit of the reliability, and the other is the limit error of the difference between the maximal value and the second-maximal value of the reliability. A real-time map-matching system based on fuzzy logic is therefore developed. Using the real data of global positioning system(GIS) based navigation and geographic information system(GPS) based road map, the method is verified and the results prove the effectiveness of the proposed method.
Fuzzy Algorithm for Supervisory Voltage/Frequency Control of a Self Excited Induction Generator
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Hussein F. Soliman
2006-01-01
Full Text Available This paper presents the application of a Fuzzy Logic Controller (FLC to regulate the voltage of a Self Excited Induction Generator (SEIG driven by Wind Energy Conversion Schemes (WECS. The proposed FLC is used to tune the integral gain (KI of a Proportional plus Integral (PI controller. Two types of controls, for the generator and for the wind turbine, using a FLC algorithm, are introduced in this paper. The voltage control is performed to adapt the terminal voltage via self excitation. The frequency control is conducted to adjust the stator frequency through tuning the pitch angle of the WECS blades. Both controllers utilize the Fuzzy technique to enhance the overall dynamic performance. The simulation result depicts a better dynamic response for the system under study during the starting period, and the load variation. The percentage overshoot, rising time and oscillation are better with the fuzzy controller than with the PI controller type.
Research on the fully fuzzy time-cost trade-off based on genetic algorithms
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
It is very difficult to estimate exact values of time and cost of an activity in project scheduling process because many uncertain factors, such as weather, productivity level, human factors etc. , dynamically affect them during project implementation process. A GAs-based fully fuzzy optimal time-cost trade-off model is presented based on fuzzy sets and genetic algorithms (GAs). In tihs model all parameters and variables are characteristics by fuzzy numbers. And then GAs is adopted to search for the optimal solution to this model. The method solves the time-cost trade-off problems under an uncertain environment and is proved practicable through a giving example in ship building scheduling.
ISS-based robust adaptive fuzzy algorithm for maintaining a ship's track
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
This paper focuses on the problem of linear track keeping for marine surface vessels.The influence exerted by sea currents on the kinematic equation of ships is considered first.The input-to-state stability (ISS) theory used to verify the system is input-to-state stable.Combining the Nussbaum gain with backstepping techniques, a robust adaptive fuzzy algorithm is presented by employing fuzzy systems as an approximator for unknown nonlinearities in the system.It is proved that the proposed algorithm that guarantees all signals in the closed-loop system are ultimately bounded.Consequently, a ship's linear track-keeping control can be implemented.Simulation results using Dalian Maritime University's ocean-going training ship 'YULONG' are presented to validate the effectiveness of the proposed algorithm.
Application of hybrid coded genetic algorithm in fuzzy neural network controller
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
Presents the fuzzy neural network optimized by hybrid coded genetic algorithm of decimal encoding and bi nary encoding, the searching ability and stability of genetic algorithms enhanced by using binary encoding during the crossover operation and decimal encoding during the mutation operation, and the way of accepting new individuals by probability adopted, by which a new individual is accepted and its parent is discarded when its fitness is higher than that of its parent, and a new individual is accepted by probability when its fitness is lower than that of its parent. And concludes with calculations made with an example that these improvements enhance the speed of genetic algorithms to optimize the fuzzy neural network controller.
Adaptive control of parallel manipulators via fuzzy-neural network algorithm
Institute of Scientific and Technical Information of China (English)
Dachang ZHU; Yuefa FANG
2007-01-01
This paper considers adaptive control of parallel manipulators combined with fuzzy-neural network algorithms (FNNA). With this algorithm, the robustness is guaranteed by the adaptive control law and the parametric uncertainties are eliminated. FNNA is used to handle model uncertainties and external disturbances. In the proposed control scheme,we consider modifying the weight of fuzzy rules and present these rules to a MIMO system of parallel manipulators with more than three degrees-of-freedom (DoF). The algorithm has the advantage of not requiring the inverse of the Jacobian matrix especially for the low DoF parallel manipulators. The validity of the control scheme is shown through numerical simulations of a 6-RPS parallel manipulator with three DoF.
A DNA sequence alignment algorithm using quality information and a fuzzy inference method
Institute of Scientific and Technical Information of China (English)
Kwangbaek Kim; Minhwan Kim; Youngwoon Woo
2008-01-01
DNA sequence alignment algorithms in computational molecular biology have been improved by diverse methods.In this paper.We propose a DNA sequence alignment that Uses quality information and a fuzzy inference method developed based on the characteristics of DNA fragments and a fuzzy logic system in order to improve conventional DNA sequence alignment methods that uses DNA sequence quality information.In conventional algorithms.DNA sequence alignment scores are calculated by the global sequence alignment algorithm proposed by Needleman-Wunsch,which is established by using quality information of each DNA fragment.However,there may be errors in the process of calculating DNA sequence alignment scores when the quality of DNA fragment tips is low.because only the overall DNA sequence quality information are used.In our proposed method.an exact DNA sequence alignment can be achieved in spite of the low quality of DNA fragment tips by improvement of conventional algorithms using quality information.Mapping score parameters used to calculate DNA sequence alignment scores are dynamically adjusted by the fuzzy logic system utilizing lengths of DNA fragments and frequencies of low quality DNA bases in the fragments.From the experiments by applying real genome data of National Center for Bioteclmology Information,we could see that the proposed method is more efficient than conventional algorithms.
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Saad M. Darwish
2016-10-01
Full Text Available Quantitative multilevel association rules mining is a central field to realize motivating associations among data components with multiple levels abstractions. The problem of expanding procedures to handle quantitative data has been attracting the attention of many researchers. The algorithms regularly discretize the attribute fields into sharp intervals, and then implement uncomplicated algorithms established for Boolean attributes. Fuzzy association rules mining approaches are intended to defeat such shortcomings based on the fuzzy set theory. Furthermore, most of the current algorithms in the direction of this topic are based on very tiring search methods to govern the ideal support and confidence thresholds that agonize from risky computational cost in searching association rules. To accelerate quantitative multilevel association rules searching and escape the extreme computation, in this paper, we propose a new genetic-based method with significant innovation to determine threshold values for frequent item sets. In this approach, a sophisticated coding method is settled, and the qualified confidence is employed as the fitness function. With the genetic algorithm, a comprehensive search can be achieved and system automation is applied, because our model does not need the user-specified threshold of minimum support. Experiment results indicate that the recommended algorithm can powerfully generate non-redundant fuzzy multilevel association rules.
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A. Hajnoori
2013-07-01
Full Text Available Portfolio optimization problem follows the calculation of investment income per share, based on return and risk criteria. Since stock risk is achieved by calculating its return, which is itself computed based on stock price, it is essential to forecast the stock price, efficiently. In this paper, in order to predict the stock price, grey fuzzy technique with high efficiency is employed. The proposed study of this paper calculates the return and risk of each asset and portfolio optimization model is developed based on cardinality constraint and investment income per share. To solve the resulted model, Invasive Weed Optimization (IWO algorithm is applied. In an example this algorithm is compared with other metaheuristic algorithms such as Imperialist Competitive Algorithm (ICA, Genetic Algorithm (GA and Particle Swarm Optimization (PSO. The results show that the applied algorithm performs significantly better than other algorithms.
Rajabi, Mohammad Mahdi; Ataie-Ashtiani, Behzad
2016-05-01
Bayesian inference has traditionally been conceived as the proper framework for the formal incorporation of expert knowledge in parameter estimation of groundwater models. However, conventional Bayesian inference is incapable of taking into account the imprecision essentially embedded in expert provided information. In order to solve this problem, a number of extensions to conventional Bayesian inference have been introduced in recent years. One of these extensions is 'fuzzy Bayesian inference' which is the result of integrating fuzzy techniques into Bayesian statistics. Fuzzy Bayesian inference has a number of desirable features which makes it an attractive approach for incorporating expert knowledge in the parameter estimation process of groundwater models: (1) it is well adapted to the nature of expert provided information, (2) it allows to distinguishably model both uncertainty and imprecision, and (3) it presents a framework for fusing expert provided information regarding the various inputs of the Bayesian inference algorithm. However an important obstacle in employing fuzzy Bayesian inference in groundwater numerical modeling applications is the computational burden, as the required number of numerical model simulations often becomes extremely exhaustive and often computationally infeasible. In this paper, a novel approach of accelerating the fuzzy Bayesian inference algorithm is proposed which is based on using approximate posterior distributions derived from surrogate modeling, as a screening tool in the computations. The proposed approach is first applied to a synthetic test case of seawater intrusion (SWI) in a coastal aquifer. It is shown that for this synthetic test case, the proposed approach decreases the number of required numerical simulations by an order of magnitude. Then the proposed approach is applied to a real-world test case involving three-dimensional numerical modeling of SWI in Kish Island, located in the Persian Gulf. An expert
Image Retrieval Approach Based on Intuitive Fuzzy Set Combined with Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
WANG Xiao-yin; XU Wei-hua; HU Chang-zhen
2009-01-01
Aiming at shortcomings of traditional image retrieval systems,a new image retrieval approach based on color features of image combining intuitive fuzzy theory with genetic algorithm is proposed.Each image is segmented into a constant number of sub-images in vertical direction.Color features are extracted from every sub-image to get chromosome coding.It is considered that fuzzy membership and intuitive fuzzy hesitancy degree of every pixel's color in image are associated to all the color histogram bins.Certain feature,fuzzy feature and intuitive fuzzy feature of colors in an image,are used together to describe the content of image.Efficient combinations of sub-image are selected according to operation of selecting,crossing and variation.Retrieval resuits are obtained from image matching based on these color feature combinations of sub-images.Tests show that this approach can improve the accuracy of image retrieval in the case of not decreasing the speed of image retrieval.Its mean precision is above 80%.
Analyzing Job Aware Scheduling Algorithm in Hadoop for Heterogeneous Cluster
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Mayuri A Mehta
2015-12-01
Full Text Available A scheduling algorithm is required to efficiently manage cluster resources in a Hadoop cluster, thereby to increase resource utilization and to reduce response time. The job aware scheduling algorithm schedules non-local map tasks of jobs based on job execution time, earliest deadline first or workload of the job. In this paper, we present the performance evaluation of the job aware scheduling algorithm using MapReduce WordCount benchmark. The experimental results are compared with matchmaking scheduling algorithm. The results show that the job aware scheduling algorithm reduces average waiting time and memory wastage considerably as compared to matchmaking algorithm.
Study of the Artificial Fish Swarm Algorithm for Hybrid Clustering
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Hongwei Zhao
2015-06-01
Full Text Available The basic Artificial Fish Swarm (AFS Algorithm is a new type of an heuristic swarm intelligence algorithm, but it is difficult to optimize to get high precision due to the randomness of the artificial fish behavior, which belongs to the intelligence algorithm. This paper presents an extended AFS algorithm, namely the Cooperative Artificial Fish Swarm (CAFS, which significantly improves the original AFS in solving complex optimization problems. K-medoids clustering algorithm is being used to classify data, but the approach is sensitive to the initial selection of the centers with low quality of the divided cluster. A novel hybrid clustering method based on the CAFS and K-medoids could be used for solving clustering problems. In this work, first, CAFS algorithm is used for optimizing six widely-used benchmark functions, coming up with comparative results produced by AFS and CAFS, then Particle Swarm Optimization (PSO is studied. Second, the hybrid algorithm with K-medoids and CAFS algorithms is used for data clustering on several benchmark data sets. The performance of the hybrid algorithm based on K-medoids and CAFS is compared with AFS and CAFS algorithms on a clustering problem. The simulation results show that the proposed CAFS outperforms the other two algorithms in terms of accuracy and robustness.
Directory of Open Access Journals (Sweden)
Lingli Jiang
2011-01-01
Full Text Available This paper proposes a new approach combining autoregressive (AR model and fuzzy cluster analysis for bearing fault diagnosis and degradation assessment. AR model is an effective approach to extract the fault feature, and is generally applied to stationary signals. However, the fault vibration signals of a roller bearing are non-stationary and non-Gaussian. Aiming at this problem, the set of parameters of the AR model is estimated based on higher-order cumulants. Consequently, the AR parameters are taken as the feature vectors, and fuzzy cluster analysis is applied to perform classification and pattern recognition. Experiments analysis results show that the proposed method can be used to identify various types and severities of fault bearings. This study is significant for non-stationary and non-Gaussian signal analysis, fault diagnosis and degradation assessment.
Road Surface Modeling and Representation from Point Cloud Based on Fuzzy Clustering
Institute of Scientific and Technical Information of China (English)
ZHANG Yi; YAN Li
2007-01-01
A scheme for an automatic road surface modeling from a noisy point cloud is presented. The normal vectors of the point cloud are estimated by distance-weighted fitting of local plane. Then, an automatic recognition of the road surface from noise is performed based on the fuzzy clustering of normal vectors, with which the mean value is calculated and the projecting plane of point cloud is created to obtain the geometric model accordingly. Based on fuzzy clustering of the intensity attributed to each point, different objects on the road surface are assigned different colors for representing abundant appearances.This unsupervised method is demonstrated in the experiment and shows great effectiveness in reconstructing and rendering better road surface.
Automated maneuver planning using a fuzzy logic algorithm
Conway, D.; Sperling, R.; Folta, D.; Richon, K.; Defazio, R.
1994-01-01
Spacecraft orbital control requires intensive interaction between the analyst and the system used to model the spacecraft trajectory. For orbits with right mission constraints and a large number of maneuvers, this interaction is difficult or expensive to accomplish in a timely manner. Some automation of maneuver planning can reduce these difficulties for maneuver-intensive missions. One approach to this automation is to use fuzzy logic in the control mechanism. Such a prototype system currently under development is discussed. The Tropical Rainfall Measurement Mission (TRMM) is one of several missions that could benefit from automated maneuver planning. TRMM is scheduled for launch in August 1997. The spacecraft is to be maintained in a 350-km circular orbit throughout the 3-year lifetime of the mission, with very small variations in this orbit allowed. Since solar maximum will occur as early as 1999, the solar activity during the TRMM mission will be increasing. The increasing solar activity will result in orbital maneuvers being performed as often as every other day. The results of automated maneuver planning for the TRMM mission will be presented to demonstrate the prototype of the fuzzy logic tool.
Cluster fusion algorithm: application to Lennard-Jones clusters
DEFF Research Database (Denmark)
Solov'yov, Ilia; Solov'yov, Andrey V.; Greiner, Walter
2008-01-01
paths up to the cluster size of 150 atoms. We demonstrate that in this way all known global minima structures of the Lennard-Jones clusters can be found. Our method provides an efficient tool for the calculation and analysis of atomic cluster structure. With its use we justify the magic number sequence...... for the clusters of noble gas atoms and compare it with experimental observations. We report the striking correspondence of the peaks in the dependence of the second derivative of the binding energy per atom on cluster size calculated for the chain of the Lennard-Jones clusters based on the icosahedral symmetry......We present a new general theoretical framework for modelling the cluster structure and apply it to description of the Lennard-Jones clusters. Starting from the initial tetrahedral cluster configuration, adding new atoms to the system and absorbing its energy at each step, we find cluster growing...
Cluster fusion algorithm: application to Lennard-Jones clusters
DEFF Research Database (Denmark)
Solov'yov, Ilia; Solov'yov, Andrey V.; Greiner, Walter
2006-01-01
paths up to the cluster size of 150 atoms. We demonstrate that in this way all known global minima structures of the Lennard-Jones clusters can be found. Our method provides an efficient tool for the calculation and analysis of atomic cluster structure. With its use we justify the magic number sequence...... for the clusters of noble gas atoms and compare it with experimental observations. We report the striking correspondence of the peaks in the dependence of the second derivative of the binding energy per atom on cluster size calculated for the chain of the Lennard-Jones clusters based on the icosahedral symmetry......We present a new general theoretical framework for modelling the cluster structure and apply it to description of the Lennard-Jones clusters. Starting from the initial tetrahedral cluster configuration, adding new atoms to the system and absorbing its energy at each step, we find cluster growing...