The goal of this paper was to apply fuzzyclusteringalgorithm known as fuzzyc-means to color image segmentation, which is an important problem in pattern recognition and computer vision. For computational experiment...
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ISBN:
(纸本)9788360810668
The goal of this paper was to apply fuzzyclusteringalgorithm known as fuzzyc-means to color image segmentation, which is an important problem in pattern recognition and computer vision. For computational experiments, serial and parallel versions were implemented. Both were tested using various parameters and random number generator seeds. Various distance measures were used: Euclidean, Manhattan metrics and two versions of Gower coefficient similarity measure. The F and Q segmentation evaluation measures and output images were used to assess the result of color segmentation. Serial and parallel run times were compared.
The authors propose a novel multi-model direct generalised predictive control based on predictive function control (PFc) algorithm for automatic train operation system. The proposed method facilitates autonomous drivi...
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The authors propose a novel multi-model direct generalised predictive control based on predictive function control (PFc) algorithm for automatic train operation system. The proposed method facilitates autonomous driving of a train through a given guidance trajectory. Firstly, they present a multi-model architecture based on fuzzy c-means clustering algorithm. In order to obtain the optimal number of sub-linear models, they apply Xie-Beni cluster validity index. In this regards, the multi-model set is established off-line. Secondly, the proper sub-linear model is selected as the predictive model by using switching performance index at each time slot. The control variables are calculated by direct generalised predictive controller based on PFc. The control algorithm is simple, and can reduce the on-line computation time by directly identifies the unknown parameters in the controller. It can avoid recursively solving the Diophantine equations. The calculation of compensation value becomes simple by introducing PFc. Finally, simulation results are provided to show the effectiveness of the proposed scheme.
A robust fuzzycontrol design, with time delay, based on gain and phase margins specifications for nonlinear systems, in the continuous time delay, is proposed. From input and output data of the process, a fuzzyc-Mea...
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ISBN:
(纸本)9781479983896
A robust fuzzycontrol design, with time delay, based on gain and phase margins specifications for nonlinear systems, in the continuous time delay, is proposed. From input and output data of the process, a fuzzyc-means (FcM) clusteringalgorithm estimates the antecedent parameters and the rules number of a Takagi-Sugeno fuzzy model, whereas the least squares algorithm estimates the consequent parameters. A multiobjective genetic strategy is developed to tune the fuzzy digital controller parameters, so the gain and phase specified margins are obtained for the fuzzycontrol system. The fuzzy PID controller was implemented on a real time acquisition data platform, based on compactRIO (NI cRIO-9073) and LabVIEW, from National Instruments, for temperature control of a thermic process. The experimental results show the efficiency of the proposed methodology through the accuracy in the gain and phase margins of the PID control system compared to the specified ones and tracking of the reference trajectory. fuzzy PID controller also is more efficient when compared to the fuzzy delay and lead compensator.
The rationale behind ensemble machine learning systems is the creation of many classifiers and the combination of their output such that the combination improves the performance of each single classifier. There are tw...
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ISBN:
(纸本)9781467374286
The rationale behind ensemble machine learning systems is the creation of many classifiers and the combination of their output such that the combination improves the performance of each single classifier. There are two key issues in the creation of ensemble classifiers: one is how two select and group the data samples to train the individual models and the other is how to select or combine the multiple outputs. In this work, the fuzzy c-means clustering algorithm is used to identify groups of samples with common characteristics, and each group is used to develop individual fuzzyclassifiers. The final classification of the ensemble fuzzyclassifier is obtained using two different output aggregation decision criteria. The two decision criteria proposed are based on the arithmetic mean of the multiple classifiers output and on the fuzzy weighted average of the output of each classifier with the distance to the corresponding cluster. The objective of this work is to see if greater predictive performance can be achieved using multiple output aggregation techniques (arithmetic mean and distance-weighted mean criteria) as compared to selection techniques based on distance metrics to select which classifier to be used. The performance of the presented approach is tested using two real world clinical databases and two benchmark datasets from the UcI benchmark repository. Results show that the distance-weighted mean performs better than the arithmetic mean criteria. Overall, no significant difference was found between the performance of aggregation and selection classifiers.
In this paper,a new radial basis function(RBF) neural network with fuzzy c-means clustering algorithm based on geneticalgorithm(GA) is proposed for the fault diagnosis of gyroscopes and accelerometers of strapdown in...
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In this paper,a new radial basis function(RBF) neural network with fuzzy c-means clustering algorithm based on geneticalgorithm(GA) is proposed for the fault diagnosis of gyroscopes and accelerometers of strapdown inertial navigation system(SINS).The fuzzyc-meansalgorithm(FcM) tends to fall into the local *** fuzzy c-means clustering algorithmcombined with GA(FGA) obtains the global optimal cluster *** is used to provide the optimal cluster centers for RBF neural network,and a second order learning algorithm is used to train the parameters and weights of RBF neural *** results show that the proposed RBF neural network with FGA quickly converges and effectively improves the diagnostic accuracy rate of SINS fault diagnosis.
Among financial time-series analysis tasks, stock index forecasting has been considered as one of the challenging and difficult tasks. Since an accurate stock index prediction enhances stock market returns, it is high...
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ISBN:
(纸本)9781467374293
Among financial time-series analysis tasks, stock index forecasting has been considered as one of the challenging and difficult tasks. Since an accurate stock index prediction enhances stock market returns, it is highly promising research area and has attracted particular attention. In this paper, to predict stock index with complex and nonlinear characteristics, an automatic structure identification (SI) method of TSK fuzzy model is proposed. Typically, SI procedures of fuzzy models consist of relevant input selection, fuzzy rule generation and parameter search space determination. In this study, mutual information is employed to select relevant input variables and fuzzyc-means (FcM) clusteringalgorithm is used to generate fuzzy if-then rules. In FcM clustering, the number of clusters should be fixed in advance. This paper uses performance criterion to determine the optimal number of clusters in FcM clustering. After deciding the optimal cluster number, fuzzy if-then rules are extracted and parameter search space boundaries are fixed. Finally, premise and consequent parameters are optimized by cooperative random learning particle swarm optimization proposed by Zhao et al. To confirm the effectiveness, the proposed TSK fuzzy modeling method and comparison methods are applied to Korea composite Stock Price Index dataset. The experimental results show that the TSK fuzzy models with the proposed SI method outperform comparison methods.
fuzzyc-means (FcMs) with spatial constraints have been considered as an effective algorithm for image segmentation. The well-known Gaussian mixture model (GMM) has also been regarded as a useful tool in several image...
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fuzzyc-means (FcMs) with spatial constraints have been considered as an effective algorithm for image segmentation. The well-known Gaussian mixture model (GMM) has also been regarded as a useful tool in several image segmentation applications. In this study, the authors propose a new algorithm to incorporate the merits of these two approaches and reveal some intrinsic relationships between them. In the authors model, the new objective function pays more attention on spatial constraints and adopts Gaussian distribution as the distance function. Thus, their model can degrade to the standard GMM as a special case. Our algorithm is fully free of the empirically pre-defined parameters that are used in traditional FcM methods to balance between robustness to noise and effectiveness of preserving the image sharpness and details. Furthermore, in their algorithm, the prior probability of an image pixel is influenced by the fuzzy memberships of pixels in its immediate neighbourhood to incorporate the local spatial information and intensity information. Finally, they utilise the mean template instead of the traditional hidden Markov random field (HMRF) model for estimation of prior probability. The mean template is considered as a spatial constraint for collecting more image spatial information. compared with HMRF, their method is simple, easy and fast to implement. The performance of their proposed algorithm, compared with state-of-the-art technologies including extensions of possibilisticfuzzyc-means (PFcM), GMM, FcM, HMRF and their hybrid models, demonstrates its improved robustness and effectiveness.
In this study, the concepts of competitive agglomeration clusteringalgorithm is incorporated into fuzzyc-means (FcM) clusteringalgorithm for symbolic interval-values data. In the proposed approach, called as IFcMwU...
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ISBN:
(纸本)9784907764302
In this study, the concepts of competitive agglomeration clusteringalgorithm is incorporated into fuzzyc-means (FcM) clusteringalgorithm for symbolic interval-values data. In the proposed approach, called as IFcMwUNcclusteringalgorithm, the problems of the unknown clusters number and the initialization of prototypes in the FcM clusteringalgorithm for symbolic interval-values data are overcome and discussed. Due to the competitive agglomeration clusteringalgorithm possess the advantages of the hierarchical clusteringalgorithm and the partitional clusteringalgorithm, IFcMwUNcclusteringalgorithmcan be fast converges in a few iterations regardless of the initial number of clusters. Moreover, it is also converges to the same optimal partition regardless of its initialization. Experiments results show the merits and usefulness of IFcMwUNcclusteringalgorithm for the symbolic interval-values data.
This study presents a comprehensive approach to tackle the problem of optimal placement and coordinated tuning of power system supplementary damping controllers (OPcTSDc). The approach uses a recursive framework compr...
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This study presents a comprehensive approach to tackle the problem of optimal placement and coordinated tuning of power system supplementary damping controllers (OPcTSDc). The approach uses a recursive framework comprising probabilistic eigenanalysis (PE), a scenario selection technique (SST) and a new variant of mean-variance mapping optimisation algorithm (MVMO-SM). Based on probabilistic models used to sample a wide range of operating conditions, PE is applied to determine the instability risk because of poorly-damped oscillatory modes. Next, the insights gathered from PE are exploited by SST, which combines principal component analysis and fuzzy c-means clustering algorithm to extract a reduced subset of representative scenarios. The multi-scenario formulation of OPcTSDc is then solved by MVMO-SM. A case study on the New England test system, which includes performance comparisons between different modern heuristic optimisation algorithms, illustrates the feasibility and effectiveness of the proposed approach.
Life of modern people becomes more convenient and rich in material side but worse in mental side due to life stress. This results in bloom of some diseases such as insomnia. Listening to musiccould be one way to make...
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ISBN:
(纸本)9781614994404;9781614994398
Life of modern people becomes more convenient and rich in material side but worse in mental side due to life stress. This results in bloom of some diseases such as insomnia. Listening to musiccould be one way to make people feel smooth. Some previous literature had advocated the efficiency of music therapy, however, only a few previous studies discussed and connected personal cognition (subjective indicators) with music features (objective indicators). Therefore, the aim of the study is to investigate what kind of musiccharacteristics can spiritually relax people and obtain the therapeutic music from above results. Firstly, this study collected 25 different styles of music as samples. These songs were classified with fuzzy c-means clustering algorithm. According to our experimental result, music with mild amplitude, slow speed, and positive feelings can enable soothing in mind. The findings would also fit in with physiological signals (Heart Rate Variability) to ensure the consistency in psychology and physiology. This finding can provide suggestions on selection of therapeutic music. In addition, musicians can compose appropriate therapeutic music for patients of different mental illness.
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