Relevant bibliographies by topics / Fuzzy C Means Clustering for Driving Data Pattern Recognition

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  2. Dissertations / Theses
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Academic literature on the topic 'Fuzzy C Means Clustering for Driving Data Pattern Recognition'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 February 2022

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Journal articles on the topic "Fuzzy C Means Clustering for Driving Data Pattern Recognition"

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Ferdaus, Md Meftahul, Sreenatha G. Anavatti, Matthew A. Garratt, and Mahardhika Pratama. "Development of C-Means Clustering Based Adaptive Fuzzy Controller for a Flapping Wing Micro Air Vehicle." Journal of Artificial Intelligence and Soft Computing Research 9, no. 2 (2019): 99–109. http://dx.doi.org/10.2478/jaiscr-2018-0027.

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Abstract Advanced and accurate modelling of a Flapping Wing Micro Air Vehicle (FW MAV) and its control is one of the recent research topics related to the field of autonomous MAVs. Some desiring features of the FW MAV are quick flight, vertical take-off and landing, hovering, and fast turn, and enhanced manoeuvrability contrasted with similar-sized fixed and rotary wing MAVs. Inspired by the FW MAV’s advanced features, a four-wing Nature-inspired (NI) FW MAV is modelled and controlled in this work. The Fuzzy C-Means (FCM) clustering algorithm is utilized to construct the data-driven NIFW MAV model. Being model free, it does not depend on the system dynamics and can incorporate various uncertainties like sensor error, wind gust etc. Furthermore, a Takagi-Sugeno (T-S) fuzzy structure based adaptive fuzzy controller is proposed. The proposed adaptive controller can tune its antecedent and consequent parameters using FCM clustering technique. This controller is employed to control the altitude of the NIFW MAV, and compared with a standalone Proportional Integral Derivative (PID) controller, and a Sliding Mode Control (SMC) theory based advanced controller. Parameter adaptation of the proposed controller helps to outperform it static PID counterpart. Performance of our controller is also comparable with its advanced and complex counterpart namely SMC-Fuzzy controller.
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Li, Dan, Chong Quan Zhong, and Shi Qiang Wang. "A Fuzzy C-Means Approach for Incomplete Data Sets Based on Nearest-Neighbor Intervals." Applied Mechanics and Materials 411-414 (September 2013): 1108–11. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.1108.

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Partially missing data sets are a prevailing problem in pattern recognition. In this paper, the problem of clustering incomplete data sets is considered, and missing attribute values are imputed by the centers of corresponding nearest-neighbor intervals. Firstly, the algorithm estimates the nearest-neighbor intervals of missing attribute values by using the attribute distribution information of the data sets sufficiently. Secondly, the missing attribute values are imputed by the center of the intervals so as to clustering incomplete data sets. The proposed algorithm introduces the nearest neighbor information into incomplete data clustering, and the comparisons of the experimental results for two UCI data sets demonstrate the capability of the proposed algorithm.
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Kanirajan, P., M. Joly, and T. Eswaran. "Recognition of Power Quality Disturbances using Fuzzy Expert Systems." WSEAS TRANSACTIONS ON SIGNAL PROCESSING 16 (January 19, 2021): 166–77. http://dx.doi.org/10.37394/232014.2020.16.18.

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This paper presents a new approach to detect and classify power quality disturbances in the power system using Fuzzy C-means clustering, Fuzzy logic (FL) and Radial basis Function Neural Networks (RBFNN). Feature extracted through wavelet is used for training, after training, the obtained weight is used to classify the power quality problems in RBFNN, but it suffers from extensive computation and low convergence speed. Then to detect and classify the events, FL is proposed, the extracted characters are used to find out membership functions and fuzzy rules being determined from the power quality inherence. For the classification,5 types of disturbance are taken in to account. The classification performance of FL is compared with RBFNN.The clustering analysis is used to group the data in to clusters to identifying the class of the data with Fuzzy C-means algorithm. The classification accuracy of FL and Fuzzy C-means clustering is improved with the help of cognitive as well as the social behavior of particles along with fitness value using Particle swarm optimization (PSO),just by determining the ranges of the feature of the membership funtion for each rules to identify each disturbance specifically.The simulation result using Fuzzy C-means clustering possess significant improvements and gives classification results in less than a cycle when compared over other considered approach.
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Marlinda, Linda, Muhamad Fatchan, Widiyawati Widiyawati, Faruq Aziz, and Wahyu Indrarti. "Segmentation of Mango Fruit Image Using Fuzzy C-Means." SinkrOn 5, no. 2 (2021): 275–81. http://dx.doi.org/10.33395/sinkron.v5i2.10933.

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Mango contains about 20 vitamins and minerals such as iron, copper, potassium, phosphorus, zinc, and calcium. The freshness of the ripe mango will taste sweet. The level of ripeness of the mango fruit can be seen from the texture of the skin and skin color. Ripe mangoes have a bright, fragrant color and a smooth skin texture. The problem found in mango segmentation is that the image of the mango fruit is influenced by several factors, such as noise and environmental objects. In measuring the maturity of mangoes traditionally, it can be seen from image analysis based on skin color. The mango peel segmentation process is needed so that the classification or pattern recognition process can be carried out better. The segmented mango image will read the feature extraction value of an object that has been separated from the background. The procedure on the image that has been analyzed will analyze the pattern recognition process. In this process, the segmented image is divided into several parts according to the desired object acquisition. Clustering is a technique for segmenting images by grouping data according to class and partitioning the data into mango datasets. This study uses the Fuzzy C Means method to produce optimal results in determining the clustering-based image segmentation. The final result of Fuzzy C-based mango segmentation processing means that the available feature extraction value or equal to the maximum number of iterations (MaxIter) is 31 iterations, error (x) = 0.00000001, and the image computation testing time is 2444.913636
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Li, Enwen, Linong Wang, Bin Song, and Siliang Jian. "Improved Fuzzy C-Means Clustering for Transformer Fault Diagnosis Using Dissolved Gas Analysis Data." Energies 11, no. 9 (2018): 2344. http://dx.doi.org/10.3390/en11092344.

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Dissolved gas analysis (DGA) of the oil allows transformer fault diagnosis and status monitoring. Fuzzy c-means (FCM) clustering is an effective pattern recognition method, but exhibits poor clustering accuracy for dissolved gas data and usually fails to subsequently correctly classify transformer faults. The existing feasible approach involves combination of the FCM clustering algorithm with other intelligent algorithms, such as neural networks and support vector machines. This method enables good classification; however, the algorithm complexity is greatly increased. In this paper, the FCM clustering algorithm itself is improved and clustering analysis of DGA data is realized. First, the non-monotonicity of the traditional clustering membership function with respect to the sample distance and its several local extrema are discussed, which mainly explain the poor classification accuracy of DGA data clustering. Then, an exponential form of the membership function is proposed to obtain monotony with respect to distance, thereby improving the dissolved gas data clustering. Likewise, a similarity function to determine the degree of membership is derived. Test results for large datasets show that the improved clustering algorithm can be successfully applied for DGA-data-based transformer fault detection.
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Ojeda-Magaña, B., R. Ruelas, M. A. Corona Nakamura, D. W. Carr Finch, and L. Gómez-Barba. "Pattern Recognition in Numerical Data Sets and Color Images through the Typicality Based on the GKPFCM Clustering Algorithm." Mathematical Problems in Engineering 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/716753.

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We take the concept of typicality from the field of cognitive psychology, and we apply the meaning to the interpretation of numerical data sets and color images through fuzzy clustering algorithms, particularly the GKPFCM, looking to get better information from the processed data. The Gustafson Kessel Possibilistic Fuzzy c-means (GKPFCM) is a hybrid algorithm that is based on a relative typicality (membership degree, Fuzzy c-means) and an absolute typicality (typicality value, Possibilistic c-means). Thus, using both typicalities makes it possible to learn and analyze data as well as to relate the results with the theory of prototypes. In order to demonstrate these results we use a synthetic data set and a digitized image of a glass, in a first example, and images from the Berkley database, in a second example. The results clearly demonstrate the advantages of the information obtained about numerical data sets, taking into account the different meaning of typicalities and the availability of both values with the clustering algorithm used. This approach allows the identification of small homogeneous regions, which are difficult to find.
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Hou, Li Bo. "Improved Fuzzy FCM-LI Algorithm." Advanced Materials Research 765-767 (September 2013): 670–73. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.670.

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Fuzzy C-means (FCM) clustering algorithm is one of the widely applied algorithms in non-supervision of pattern recognition. However, FCM algorithm in the iterative process requires a lot of calculations, especially when feature vectors has high-dimensional, Use clustering algorithm to sub-heap, not only inefficient, but also may lead to "the curse of dimensionality." For the problem, This paper analyzes the fuzzy C-means clustering algorithm in high dimensional feature of the process, the problem of cluster center is an np-hard problem, In order to improve the effectiveness and Real-time of fuzzy C-means clustering algorithm in high dimensional feature analysis, Combination of landmark isometric (L-ISOMAP) algorithm, Proposed improved algorithm FCM-LI. Preliminary analysis of the samples, Use clustering results and the correlation of sample data, using landmark isometric (L-ISOMAP) algorithm to reduce the dimension, further analysis on the basis, obtained the final results. Finally, experimental results show that the effectiveness and Real-time of FCM-LI algorithm in high dimensional feature analysis.
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D'URSO, PIERPAOLO. "FUZZY C-MEANS CLUSTERING MODELS FOR MULTIVARIATE TIME-VARYING DATA: DIFFERENT APPROACHES." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 12, no. 03 (2004): 287–326. http://dx.doi.org/10.1142/s0218488504002849.

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The classification of multivariate time-varying data finds application in several fields, such as economics, finance, marketing research, psychometrics, bioinformatics, medicine, signal processing, pattern recognition, etc. In this paper, by considering an exploratory formalization, we propose different unsupervised clustering models for multivariate data time arrays (objects×quantitative variables×times). These models can be classified in two different approaches: the cross sectional and the longitudinal approach. In the first case, after the objects, observed at each time, have been classified, comparison among the classifications made in different time instants will be done. In the second approach, we cluster the time trajectories of the objects; then, we obtain only one classification by comparing the instantaneous and evolutive features of the trajectories of the objects. In particular, in this work, the second approach is analyzed in detail, with reference to the so-called single and double step procedures. Geometric, correlative, instantaneous, evolutive and trend characteristics of the multivariate time arrays are taken into account in the different proposed clustering models. Furthermore, the fuzzy approach, that is particularly suitable in the dynamic classification problem, has been considered. Extensions of a cluster-validity criterion for the proposed fuzzy dynamic clustering models are also suggested. A socio-economic example concludes the paper.
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Lin, Phen-Lan, Po-Whei Huang, C. H. Kuo, and Y. H. Lai. "A size-insensitive integrity-based fuzzy c-means method for data clustering." Pattern Recognition 47, no. 5 (2014): 2042–56. http://dx.doi.org/10.1016/j.patcog.2013.11.031.

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Li, Qiaoyan, Yingcang Ma, Florentin Smarandache, and Shuangwu Zhu. "Single-Valued Neutrosophic Clustering Algorithm Based on Tsallis Entropy Maximization." Axioms 7, no. 3 (2018): 57. http://dx.doi.org/10.3390/axioms7030057.

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Data clustering is an important field in pattern recognition and machine learning. Fuzzy c-means is considered as a useful tool in data clustering. The neutrosophic set, which is an extension of the fuzzy set, has received extensive attention in solving many real-life problems of inaccuracy, incompleteness, inconsistency and uncertainty. In this paper, we propose a new clustering algorithm, the single-valued neutrosophic clustering algorithm, which is inspired by fuzzy c-means, picture fuzzy clustering and the single-valued neutrosophic set. A novel suitable objective function, which is depicted as a constrained minimization problem based on a single-valued neutrosophic set, is built, and the Lagrange multiplier method is used to solve the objective function. We do several experiments with some benchmark datasets, and we also apply the method to image segmentation using the Lena image. The experimental results show that the given algorithm can be considered as a promising tool for data clustering and image processing.
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Dissertations / Theses on the topic "Fuzzy C Means Clustering for Driving Data Pattern Recognition"

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Munthikodu, Sreejith. "Driving data pattern recognition for intelligent energy management of plug-in hybrid electric vehicles." Thesis, 2019. http://hdl.handle.net/1828/11052.

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This work focuses on the development and testing of new driving data pattern recognition intelligent system techniques to support driver adaptive, real-time optimal power control and energy management of hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs). A novel, intelligent energy management approach that combines vehicle operation data acquisition, driving data clustering and pattern recognition, cluster prototype based power control and energy optimization, and real-time driving pattern recognition and optimal energy management has been introduced. The method integrates advanced machine learning techniques and global optimization methods form the driver adaptive optimal power control and energy management. Fuzzy C-Means clustering algorithm is used to identify the representative vehicle operation patterns from collected driving data. Dynamic Programming (DA) based off-line optimization is conducted to obtain the optimal control parameters for each of the identified driving patterns. Artificial Neural Networks (ANN) are trained to associate each of the identified operation patterns with the optimal energy management plan to support real-time optimal control. Implementation and advantages of the new method are demonstrated using the 2012 California household travel survey data, and driver-specific data collected from the city of Victoria, BC Canada.<br>Graduate
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Book chapters on the topic "Fuzzy C Means Clustering for Driving Data Pattern Recognition"

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Magdy, Amr, and Mahmoud K. Bassiouny. "SIC-Means: A Semi-fuzzy Approach for Clustering Data Streams Using C-Means." In Artificial Neural Networks in Pattern Recognition. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12159-3_9.

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Conference papers on the topic "Fuzzy C Means Clustering for Driving Data Pattern Recognition"

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Xu, Suqin, Jie Chen, and Guoxing Gao. "Remote sensing ocean data analyses using fuzzy C-Means clustering." In Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Henri Maître, Hong Sun, Bangjun Lei, and Jufu Feng. SPIE, 2009. http://dx.doi.org/10.1117/12.833199.

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