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Fuzzy forecasting based on linear combinations of independent variables, subtractive clustering algorithm and artificial bee colony algorithm

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INFORMATION SCIENCES 2019年 484卷 350-366页

作者： Zeng, Shouzhen Chen, Shyi-Ming Teng, Mario Orlando Ningbo Univ Sch Business Ningbo 315211 Zhejiang Peoples R China Natl Taiwan Univ Sci & Technol Dept Comp Sci & Informat Engn Taipei Taiwan

In this paper, we propose a new clustering-based fuzzy time series (FTS) forecasting method based on linear combinations of independent variables, the subtractive clustering algorithm and the artificial bee colony (ABC) algorithm. The subtractive clustering algorithm is used to automatically generate clusters of historical training data and get the cluster center of each cluster. The ABC algorithm is applied to obtain the optimal neighborhood radiuses of the subtractive clustering algorithm to get the optimal cluster center of each cluster of the historical training data. Based on the obtained cluster center of each cluster, the weighted contribution of each cluster with respect to each historical training datum is calculated. Finally, the proposed method constructs the linear combinations of independent variables of the historical training data using this weighted contribution to forecast the historical testing data. The proposed method gets higher forecasting accuracy rates than the existing methods for forecasting the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), the enrollments of the University of Alabama and the daily percentage of CO2. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Artificial bee colony algorithm Fuzzy time series subtractive clustering algorithm TAIEX

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Automatic subtractive clustering algorithm Model (ASCAM) for Blind Identification of QAM Constellations 13

Automatic Subtractive Clustering Algorithm Model (ASCAM) for...

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IEEE 13th International Conference on Pattern Recognition Systems (ICPRS)

作者： Sommart, Thanapong Tuektaewskul, Vanisara Suksompong, Prapun Thammasat Univ Sch Informat Comp & Commun Technol Sirindhorn Int Inst Technol Pathum Thani Thailand

ISBN: (纸本)9798350333374

Blind System Identification (BSI) is a major pattern recognition task in many fields, including digital communication systems. Recently, many machine learning techniques have been applied to identify modulation types of signals solely by the receiver without prior knowledge of the transmission protocols. In this paper, we propose Automatic subtractive clustering algorithm Model (ASCAM), a novel hybrid approach between subtractive clustering algorithm (SCA) and deep neural network for not only classifying the order of Quadrature Amplitude Modulation (QAM) from the corrupted baseband constellations, but also estimating the original symbols' locations in their constellations. For this model, a deep neural network based on parallel convolution is used in conjunction with a negative straight-through estimator to formulate this problem as a supervised task, regardless of the non-differentiability of SCA. Our simulation demonstrates that the model works well for QAMs with Rayleigh fading and additive white Gaussian noise (AWGN), and achieves better performance than other existing methods. It also generalizes its feature extraction well, as it can maintain its performance even when some symbols in the observed constellations are not present.

关键词： Blind System Identification Quadrature Amplitude Modulation subtractive clustering algorithm Deep Neural Network Straight-Through Estimator

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Research on the subtractive clustering algorithm for mobile ad hoc network based on the Akaike information criterion

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2019年第9期15卷

作者： Banteng, Liu Yang, Haibo Chen, Qiuxia Wang, Zhangquan Zhejiang Shuren Univ Coll Informat Engn Hangzhou 310015 Zhejiang Peoples R China

Large and dense mobile ad hoc networks often meet scalability problems, the hierarchical structures are needed to achieve performance of network such as cluster control structure. clustering in mobile ad hoc networks is an organization method dividing the nodes in groups, which are managed by the nodes called cluster-heads. As far as we know, the difficulty of clustering algorithm lies in determining the number and positions of cluster-heads. In this article, the subtractive clustering algorithm based on the Akaike information criterion is proposed. First, Akaike information criterion is introduced to formulate the optimal number of the cluster-heads. Then, subtractive clustering algorithm is used in mobile ad hoc networks to get several feasible clustering schemes. Finally, the candidate schemes are evaluated by the index of minimum of the largest within-cluster distance variance to determine the optimal scheme. The results of simulation show that the performance of the proposed algorithm is superior to widely referenced clustering approach in terms of average cluster-head lifetime.

关键词： Mobile ad hoc networks subtractive clustering algorithm Akaike information criterion

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An Efficient Method for Estimating Cluster Radius of subtractive clustering Based on Genetic algorithm

An Efficient Method for Estimating Cluster Radius of Subtrac...

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IEEE 17th International Symposium on Consumer Electronics (ISCE)

作者： Shieh, Horng-Lin Chang, Po-Lun Lee, Chien-Nan St Johns Univ Dept Elect Engn New Taipei City Taiwan Lunghwa Univ Sci & Technol Dept Elect Engn Taoyuan Taiwan Oriental Inst Technol Dept Elect Engn New Taipei Taiwan

ISBN: (纸本)9781467361996

The subtractive clustering algorithm (SC) is a popular method for data clustering. But the radius of each cluster is an important factor which affects the performances of clustering results. This paper proposes an objective function for the genetic algorithm to estimate the optimal value of this parameter. Two experiments show that the proposed method can automatically obtain this parameter for the subtractive clustering algorithm.

关键词： Objective function genetic algorithm subtractive clustering algorithm clustering algorithm

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Training RBF neural networks with PSO and improved subtractive clustering algorithms

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13th International Conference on Neural Informational Processing

作者： Chen, JunYing Qin, Zheng Xi An Jiao Tong Univ Dept Comp Sci Xian 710049 Peoples R China Tsinghua Univ Sch Software Beijing 100084 Peoples R China

ISBN: (纸本)3540464816

In this paper, Particle Swarm Optimization (PSO) and improved subtractive clustering algorithm were proposed for training RBF neural networks. PSO was used to feature selection in conjunction with RBF classifiers for individual fitness evaluation. During RBF training process, supervised mean subtractive clustering algorithm (SMSCA) was used to evolve RBF networks dynamically with the selected feature subset based on PSO algorithm. Experimental results on four datasets show that RBF networks evolved by our proposed algorithm have more simple architecture and stronger generalization ability with nearly the same classification performance when compared with the networks evolved by other methods.

关键词： particle swarm optimization subtractive clustering algorithm RBF neural network feature selection

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A subtractive clustering Scheme for Text-Independent Online Writer Identification 13

A Subtractive Clustering Scheme for Text-Independent Online ...

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13th IAPR International Conference on Document Analysis and Recognition (ICDAR)

作者： Singh, Gautam Sundaram, Suresh Indian Inst Technol Guwahati Dept Elect & Elect Engn Gauhati 781039 Assam India

ISBN: (纸本)9781479918058

This paper proposes a text-independent writer identification framework for online handwritten text. The method utilizes an unsupervised learning scheme termed 'subtractive clustering' to discover the unique writing styles of a given author. subtractive clustering has been adopted in the literature for the problems of image segmentation and speaker identification. To the best of our knowledge, its applicability in the domain of writer identification is yet to be explored. Unlike traditional clustering techniques such as k-means and fuzzy c-means, the subtractive clustering algorithm does not rely on the initial choice of seed points. Instead, it locates the high density regions in the feature space, and this make this scheme an interesting exploration to capture the writing styles of an author (referred to as 'prototypes'). The discovered prototypes from the clustering algorithm are subsequently employed to score the authorship of an unknown handwritten text. In addition, inspired from the tf-idf approach used in document retrieval, we propose a modified scoring scheme for identifying the writer. The efficacy of the algorithms are evaluated on the paragraphs from the IAM-Online Handwritten Database.

关键词： handwritten character recognition image retrieval pattern clustering text detection unsupervised learning document retrieval feature space high density region location image segmentation lAM-Online Handwritten Database online handwritten text speaker identification subtractive clustering algorithm subtractive clustering scheme text-independent online writer identification unique writing styles unknown handwritten text authorship unsupervised learning scheme clustering algorithms clustering algorithms document retrieval speaker recognition Eigenvector handwritten character recognition Unsupervised learning Cluster Analysis image segmentation image retrieval Writers

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On the performance evaluation of 3PL using subtractive clustering based RBFNN and UDM

On the performance evaluation of 3PL using subtractive clust...

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IEEE International Conference on Service Operations and Logistics and Informatics

作者： Kang Yan Kang Shiying Wang Ximei Chongqing Univ Sch Software Chongqing 400044 Peoples R China Chongqing Technol & Business Univ Sch Comp Sci & Informat Technol Chongqing 400067 Peoples R China

ISBN: (纸本)9781424420124

Nowadays, the application of neural network technology in the evaluation of Third party logistics (3PL) is very limited in China. The reason lies in the difficulty to find high quality training samples for neural network self-learning. This paper adopts Uniform Design Method (UDM) to design representative, uniform and large-scale samples. And then use those samples to train the subtractive clustering based Radial Basis Function neural network (SC-RBFNN) which is applied to carry out the 3PL Evaluation. The result of the experiment shows that the generalization ability of the subtractive clustering algorithm based RBFNN combined with UDM is far better than that of traditional RBFNN. This method not only has the ability of determining the number of clusters and their values automatically and realizes non-linear approaching, but also conquers the performance limitations of traditional RBFNN. Moreover it avoids the subjectivity and uncertainty of traditional evaluation.

关键词： radial basis function neural network(RBFNN) subtractive clustering algorithm The Third party logistics(3PL) uniform design method(UDM)

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subtractive clustering and phase correlation similarity measure for endmember extraction

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INFRARED PHYSICS & TECHNOLOGY 2020年 110卷 103452-103452页

作者： Palla, Parasuram Yadav Shetty, Amba Raghavendra, B. S. Narasimhadhan, A., V Natl Inst Technol Karnataka Surathkal India

Target identification using Remote Sensing techniques saves time, cost and reduces difficulties in field investigation. The endmember is a reference spectral response of a pure pixel in the hyperspectral image and is used for object identification/classification from hyperspectral data. Quality of endmembers selected influences classification accuracy. Though there have been several algorithms proposed for endmember extraction, choosing a benchmark algorithm requires further investigation. To the best of our knowledge, similarity measures have not been explored much in the extraction of spectrally distinct signatures called endmembers. In this paper, we propose a similarity measures based subtractive clustering algorithm (SM-SCA) for endmember extraction. The objective of this paper is to explore the applicability of a SM-SCA and effectiveness of different similarity measures in endmember extraction and to compare it's performance with classical endmember extraction algorithms. Implementation on airborne hyperspectral (Samson data and AVIRIS data over Cuprite region) and synthetic data proves that SM-SCA is capable of extracting endmembers of all the materials identified in the data, with appropriate similarity measure. Experimental results show that (i) the similarity measures are potential not only to discriminate but also in extraction of different endmember signatures and (ii) the proposed SM-SCA with phase correlation similarity measure perform comparable to the classical endmember extraction algorithms in identifying endmembers.

关键词： Endmember extraction subtractive clustering algorithm Similarity measures Phase correlation

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A deep semantic segmentation-based algorithm to segment crops and weeds in agronomic color images

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Information Processing in Agriculture 2022年第3期9卷 355-364页

作者： Sovi Guillaume Sodjinou Vahid Mohammadi Amadou Tidjani Sanda Mahama Pierre Gouton Institute of Mathematics and Physical Sciences University of Aboney CalaviDangboBenin ImViA UFR Sciences et TechniquesUniversite de BourgogneFranche-ComteDijonFrance

In precision agriculture,the accurate segmentation of crops and weeds in agronomic images has always been the center of *** methods have been proposed but still the clean and sharp segmentation of crops and weeds is a challenging issue for the images with a high presence of *** work proposes a segmentation method based on the combination of semantic segmentation and K-means algorithms for the segmenta-tion of crops and weeds in color *** images of two different databases were used for the segmentation *** the thresholding technique,everything except plants was removed from the ***,semantic segmentation was applied using U-net followed by the segmentation of crops and weeds using the K-means subtractive *** comparison of segmentation performance was made for the proposed method and K-Means clustering and superpixels *** proposed algorithm pro-vided more accurate segmentation in comparison to other methods with the maximum accuracy of equivalent to 99.19%.Based on the confusion matrix,the true-positive and true-negative values were 0.9952 and 0.8985 representing the true classification rate of crops and weeds,*** results indicated that the proposed method successfully provided accurate and convincing results for the segmentation of crops and weeds in the images with a complex presence of weeds.

关键词： Weed coverage Semantic segmentation Convolutional neural network subtractive clustering algorithm Simple Linear Iterative clustering (SLIC) K-means

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Locating minimum number of PMUs for pre- and post-disturbance monitoring of power systems

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第1期13卷 127-136页

作者： Koochi, Mohammad Hossein Rezaeian Esmaeili, Saeid Ledwich, Gerard Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran Queensland Univ Technol Sch Elect Engn & Comp Sci Brisbane Qld Australia

In this study, the post-disturbance coherency of buses is added to the conventional phasor measurement unit (PMU) placement problem to find the minimum number of PMUs at different depths of unobservability. To do that, different scenarios are defined based on probabilistic parameters. Then, in each scenario, central buses are determined based on the similarity of post-disturbance variations using subtractive clustering algorithm. The central bus in each area is the bus that its post-disturbance variations are similar to the highest number of buses in the area. The PMU placement problem is then solved considering the number of scenarios where each bus has been selected as a central bus. The resulted placement scheme is more suitable for monitoring the power system dynamics compared to the conventional method. In order to reduce the number of PMUs, the placement problem is solved for higher depths of unobservability. In each depth, the voltage phasor of unobservable buses is obtained using state estimation methods. The results of applying the proposed methodology on 68-bus, 16-machine system show its better performance compared to the conventional method.

关键词： phasor measurement pattern clustering power system state estimation probability power system reliability PMU placement problem predisturbance monitoring post-disturbance monitoring power system dynamics post-disturbance coherency phasor measurement unit placement problem probabilistic parameters post-disturbance variations subtractive clustering algorithm voltage phasor unobservable buses state estimation methods 68-bus system 16-machine system

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