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Fast global kernel fuzzy c-means clustering algorithm for consonant/vowel segmentation of speech signal

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Journal of Zhejiang University-Science c(computers and Electronics) 2014年第7期15卷 551-563页

作者： Xian ZANG Felipe P. VISTA IV Kil To cHONG Department of Electronic Engineering Jeonbuk National University Advanced Electronics and Information Research center Jeonbuk National University

We propose a novel clustering algorithm using fast global kernel fuzzy c-means-F(FGKFcM-F), where F refers to kernelized feature space. This algorithm proceeds in an incremental way to derive the near-optimal solution by solving all intermediate problems using kernel-based fuzzy c-means-F(KFcM-F) as a local search procedure. Due to the incremental nature and the nonlinear properties inherited from KFcM-F, this algorithm overcomes the two shortcomings of fuzzy c-means(FcM): sen- sitivity to initialization and inability to use nonlinear separable data. An accelerating scheme is developed to reduce the compu-tational complexity without significantly affecting the solution quality. Experiments are carried out to test the proposed algorithm on a nonlinear artificial dataset and a real-world dataset of speech signals for consonant/vowel segmentation. Simulation results demonstrate the effectiveness of the proposed algorithm in improving clustering performance on both types of datasets.

关键词： fuzzy c-means clustering Kernel method Global optimization consonant/vowel segmentation

A fuzzy c-means based User clustering Method for Demand Response Regulation

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A Fuzzy C-means based User Clustering Method for Demand Resp...

Investigation on gas–solid flow regimes in a novel multistage fluidized bed

第40届中国控制会议

作者： Liang Li Liang Wang Hongwei MA Peidong HAN Ziyan Li Ye Tian School of Automation Beijing Institute of Technology

The smart grid provides a good chance for the application of demand response technology, which could improve the renewable energy consumption and system stability. This paper proposes a fuzzy c-means based User clustering Method for Demand Response Regulation. fuzzy c-means clustering method is adopted to find the user set which has the closest load characteristics with the renewable energy generation. This user set has the best probability to consume renewable energy. Then,particle swarm algorithm is used to adjust the electric price, which could slightly change the electric consumption behavior of the above user set. Finally, a higher renewable energy consumption rate could be achieved under a stable and economical condition.

关键词： fuzzy c-means clustering Demand Response curve Similarity Particle Swarm Optimization

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chinese Journal of chemical Engineering 2022年第6期35卷 21-30页

作者： Gongpeng Wu Yan He college of Electromechanical Engineering Qingdao University of Science and TechnologyQingdao 266061China

Gas–solid flow regime in a novel multistage circulating fluidized bed is investigated in this study.Pressure fluctuations are first sampled from gas–solid flow systems and then are analyzed through frequency and time–frequency domain methods including power spectrum and Hilbert–Huang transform.According to the flow characteristics obtained from pressure fluctuations,it is found that the gas–solid motions in the multistage circulating fluidized bed exhibit two dominant motion peaks in low and high frequencies.Moreover,gas-cluster motions become intensive for the multistage circulating fluidized bed in comparison with the fast bed.Unlike the traditional methods,the fuzzy c-means clustering method is introduced to objectively identify flow regime in the multistage circulating fluidized bed on the basis of the flow characteristics extracted from bubbling,turbulent,fast,and multistage fluidized beds.The identification accuracy of fuzzy c-means clustering method is first verified.The identification results show that the flow regime in the multistage circulating fluidized bed is in the scope of fast flow regime under examined conditions.Moreover,the results indicate that the consistency of flow regime between two enlarged sections exists.In addition,the transition onset of fast flow regime in the multistage circulating fluidized bed is higher than that in the fast bed.

关键词： Multistage riser Pressure fluctuation Flow regime fuzzy c-means clustering Hilbert-Huang

Recognition of Blast Furnace Gas Flow center Distribution Based on Infrared Image Processing

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Journal of Iron and Steel Research(International) 2016年第3期23卷 203-209页

作者： Lin SHI You-bin WEN Guang-sheng ZHAO Tao YU School of Mathematics and Physics and Biological Engineering University of Science and Technology of Inner Mongolia School of Materials and Metallurgy University of Science and Technology of Inner Mongolia

To address the problems about the difficulty in accurate recognition of distribution features of gas flow center at blast furnace throat and determine the relationship between gas flow center distribution and gas utilization rate,a method for recognizing distribution features of blast furnace gas flow center was proposed based on infrared image processing,and distribution features of blast furnace gas flow center and corresponding gas utilization rates were categorized by using fuzzy c-means clustering and statistical methods.A concept of gas flow center offset was introduced.The results showed that,when the percentage of gas flow center without offset exceeded 85%,the average blast furnace gas utilization rate was as high as 41%;when the percentage of gas flow center without offset exceeded50%,the gas utilization rate was primarily the center gas utilization rate,and exhibited a positive correlation with no center offset degree;when the percentage of gas flow center without offset was below 50% but the sum of the percentage of gas flow center without offset and that of gas flow center with small offset exceeded 86%,the gas utilization rate depended on both the center and the edges,and was primarily the edge gas utilization rate.The method proposed was able to accurately and effectively recognize gas flow center distribution state and the relationship between it and gas utilization rate,providing evidence in favor of on-line blast furnace control.

关键词： infrared image processing gas flow center recognition gas utilization rate fuzzy c-means clustering

Integrated parallel forecasting model based on modified fuzzy time series and SVM

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Journal of Systems Engineering and Electronics 2017年第4期28卷 766-775页

作者： Yong Shuai Tailiang Song Jianping Wang Technical Support Engineering Faculty Academy of Armored Forced Engineering Beijing 100072 China Unit 68207 of the PLA Jiayuguan 735100 China china Defense Science & Technology Information center Beijing 100142 China

A dynamic parallel forecasting model is proposed, which is based on the problem of current forecasting models and their combined model. According to the process of the model, the fuzzy c-means clustering algorithm is improved in outliers operation and distance in the clusters and among the clusters. Firstly, the input data sets are optimized and their coherence is ensured, the region scale algorithm is modified and non-isometric multi scale region fuzzy time series model is built. At the same time, the particle swarm optimization algorithm about the particle speed, location and inertia weight value is improved, this method is used to optimize the parameters of support vector machine, construct the combined forecast model, build the dynamic parallel forecast model, and calculate the dynamic weight values and regard the product of the weight value and forecast value to be the final forecast values. At last, the example shows the improved forecast model is effective and accurate.

关键词： fuzzy c-means clustering fuzzy time series interval partitioning support vector machine particle swarm optimization algorithm parallel forecasting

A novel technique for the optimal design of offshore wind farm electrical layout

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Journal of Modern Power Systems and clean Energy 2013年第3期1卷 258-263页

作者： Yingying cHEN Zhaoyang DONG Ke MENG Fengji LUO Weifeng YAO Jing QIU center of Intelligent Electricity Networks(cIEN) The University of NewcastleCallaghanNSW 2308Australia University of Sydney DarlingtonNSWAustralia

The design of electrical layout is a key element in the offshore wind farm planning.We present a novel electrical layout design optimization method for offshore wind farms in this paper.The proposed method can be used to generate the network model based on fuzzy c-means(FcM)and binary integer programming(BIP)methods.It can automatically allocate wind turbines to the nearest substations and obtain the topology structure of cables utilized to connect wind turbines or turbine and substation.The objective of this optimization is to minimize the investment costs of cable connection and the transmission power losses.The results of case study clearly demonstrated the feasibility of the proposed method and showed that it can be used as a reliable tool for electrical layout design of offshore wind farms.

关键词： Electrical layout Offshore wind farm collector system fuzzy c-means clustering Binary integer programming

Instance reduction for supervised learning using input-output clustering method

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Journal of central South University 2015年第12期22卷 4740-4748页

作者： YODJAIPHET Anusorn THEERA-UMPON Nipon AUEPHANWIRIYAKUL Sansanee Electrical Engineering Department Faculty of EngineeringChiang Mai University Biomedical Engineering center Chiang Mai University computer Engineering Department Faculty of EngineeringChiang Mai University

A method that applies clustering technique to reduce the number of samples of large data sets using input-output clustering is proposed.The proposed method clusters the output data into groups and clusters the input data in accordance with the groups of output data.Then,a set of prototypes are selected from the clustered input data.The inessential data can be ultimately discarded from the data set.The proposed method can reduce the effect from outliers because only the prototypes are used.This method is applied to reduce the data set in regression problems.Two standard synthetic data sets and three standard real-world data sets are used for evaluation.The root-mean-square errors are compared from support vector regression models trained with the original data sets and the corresponding instance-reduced data sets.From the experiments,the proposed method provides good results on the reduction and the reconstruction of the standard synthetic and real-world data sets.The numbers of instances of the synthetic data sets are decreased by 25%-69%.The reduction rates for the real-world data sets of the automobile miles per gallon and the 1990 census in cA are 46% and 57%,respectively.The reduction rate of 96% is very good for the electrocardiogram(EcG) data set because of the redundant and periodic nature of EcG signals.For all of the data sets,the regression results are similar to those from the corresponding original data sets.Therefore,the regression performance of the proposed method is good while only a fraction of the data is needed in the training process.

关键词： instance reduction input-output clustering fuzzy c-means clustering support vector regression supervised learning

Diagnosis of Neem Leaf Diseases Using fuzzy-HOBINM and ANFIS Algorithms

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computers, Materials & continua 2021年第11期69卷 2061-2076页

作者： K.K.Thyagharajan I.Kiruba Raji r.m.d engineering college kavaraipettaigummidipoonditriuvallurindia

This paper proposes an approach to detecting diseases in neem leaf that uses a fuzzy-Higher Order Biologically Inspired Neuron Model(F-HOBINM)and adaptive neuro classifier(ANFIS).India exports USD 0.28-million worth of neem leaf to the UK,USA,UAE,and Europe in the form of dried leaves and powder,both of which help reduce diabetesrelated issues,cardiovascular problems,and eye disorders.Diagnosing neem leaf disease is difficult through visual interpretation,owing to similarity in their color and texture patterns.The most common diseases include bacterial blight,colletotrichum and Alternaria leaf spot,blight,damping-off,powdery mildew,Pseudocercospora leaf spot,leaf web blight,and seedling wilt.However,traditional color and texture algorithms fail to identify leaf diseases due to irregular lumps and surfaces,and rough ridges,as the classification time involved takes as long as a week.The proposed F-HOBINM algorithm recognizes the leaf intensity through the leaky capacitor,and uses subjective intensity and physical stimulus to interpret the diagnosis.Further,the processed leaf images from the HOBINM algorithm are applied to the ANFIS classifier to identify neem leaf diseases.The experimental results show 92.18%accuracy from a database of 1,462 neem leaves.

关键词： Higher-order neural network fuzzy c-means clustering Mamdani fuzzy inference system adaptive neuro-fuzzy classifier

Analytic design of information granulation-based fuzzy radial basis function neural networks with the aid of multiobjective particle swarm optimization

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International Journal of Intelligent computing and cybernetics 2012年第1期5卷 4-35页

作者： Byoung-Jun Park Jeoung-Nae choi Wook-Dong Kim Sung-Kwun Oh IT convergence Technology Research Laboratory Electronics and Telecommunications Research Institute(ETRI)DaejeonSouth Korea Research Institute KDT Co.LtdBucheong-siSouth Korea Department of Electrical Engineering The University of SuwonHwaseong-siSouth Korea

Purpose–The purpose of this paper is to consider the concept of fuzzy Radial Basis Function Neural Networks with Information Granulation(IG-FRBFNN)and their optimization realized by means of the Multiobjective Particle Swarm Optimization(MOPSO).Design/methodology/approach–In fuzzy modeling,complexity,interpretability(or simplicity)as well as accuracy of the obtained model are essential design criteria.Since the performance of the IG-RBFNN model is directly affected by some parameters,such as the fuzzification coefficient used in the FcM,the number of rules and the orders of the polynomials in the consequent parts of the rules,the authors carry out both structural as well as parametric optimization of the network.A multi-objective Particle Swarm Optimization using crowding Distance(MOPSO-cD)as well as O/WLS learning-based optimization are exploited to carry out the structural and parametric optimization of the model,respectively,while the optimization is of multiobjective character as it is aimed at the simultaneous minimization of complexity and maximization of accuracy.Findings–The performance of the proposed model is illustrated with the aid of three examples.The proposed optimization method leads to an accurate and highly interpretable fuzzy model.Originality/value–A MOPSO-cD as well as O/WLS learning-based optimization are exploited,respectively,to carry out the structural and parametric optimization of the model.As a result,the proposed methodology is interesting for designing an accurate and highly interpretable fuzzy model.

关键词： Modelling Optimization techniques Neural nets Design calculations fuzzy c-means clustering Multi-objective particle swarm optimization Information granulation-based fuzzy radial basis function neural network Ordinary least squaresmethod Weighted least square method