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Automatic circuit tuning via unsupervised learning paradigms

JOURNAL OF cIRcUITS SYSTEMS AND cOMPUTERS 2006年第2期15卷 217-242页

作者： El-Gamal, M. A. Abdel-Malek, H. L. Sorour, M. A. Cairo Univ Fac Engn Dept Engn Phys & Math Giza 12211 Egypt

This work describes a novel technique for automating the post-fabrication circuit tuning process. A training set that characterizes the behaviour of the circuit under test is first constructed. The data in this set consists of input measurement vectors with no output attributes, and is clustered via unsupervised learning algorithm in order to explore its underlying structure and correlations. The generated clusters are labeled and utilized in circuit tuning by calculating the value(s) of the tuning parameter(s). Three prominent and fundamentally different unsupervised learning algorithms, namely, the self-organizing map, the Gaussian mixture model, and the fuzzy c-means algorithm are employed and their performance is compared. The experimental results demonstrate that the proposed technique provides a robust and efficient circuit tuning approach.

关键词： circuit tuning clustering algorithms self-organizing map Gaussian mixture model fuzzy c-means algorithm

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A fuzzy clustering algorithm based on evolutionary programming

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EXPERT SYSTEMS WITH APPLIcATIONS 2009年第9期36卷 11792-11800页

作者： Dong, Hongbin Dong, Yuxin Zhou, cheng Yin, Guisheng Hou, Wei Harbin Engn Univ Harbin 150001 Peoples R China Harbin Normal Univ Dept Comp Sci Harbin 150080 Peoples R China

In this paper, a fuzzy clustering method based on evolutionary programming (EPFcM) is proposed. The algorithm benefits from the global search strategy of evolutionary programming, to improve fuzzy c-means algorithm (FcM). The cluster validity can be measured by some cluster validity indices. To increase the convergence speed of the algorithm, we exploit the modified algorithm to change the number of cluster centers dynamically. Experiments demonstrate EPFcM can find the proper number of clusters, and the result of clustering does not depend critically on the choice of the initial cluster centers. The probability of trapping into the local optima will be very lower than FcM. (c) 2009 Elsevier Ltd. All rights reserved.

关键词： fuzzy c-means algorithm Evolutionary programming cluster validity EPFcM

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Rainfall estimation from MSG images using fuzzy association rules

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JOURNAL OF INTELLIGENT & fuzzy SYSTEMS 2019年第1期37卷 1357-1369页

作者： Bouaita, Bilal Moussaoui, Abdelouahab Bachari, Nour El Islam Univ Ferhat Abbas Setif 1 Dept Comp Sci Setif Algeria Univ USTHB Dept Ecol & Environm Algiers Algeria

The Meteosat Second Generation (MSG) satellite can be used to estimate rainfall through the multispectral images, which are provided every 15 min across 12 channels. However, most studies have not maximized the terabytes of data provided by the channels in this satellite, which are potentially rich in new resources that need to be exploited. Moreover, these studies classify pixels conventionally, where a pixel is considered either 100% precipitant or 0% (no-precipitant), whereas actually it cannot be classified in a clear and unambiguous way. To address this problem, we propose a method that exploits the images of the channels and constructs an estimation model in the form of fuzzy association rules to estimate the rainfall in Northeastern Algeria. Each rule is in if (condition)-then (conclusion) form, where the condition is a combination of the various fuzzy classes of MSG images, and the conclusion contains a single fuzzy class that represents the intensities of rain: no-rain, low, moderate, and high. The obtained results are compared with the data obtained by the European Organization for the Exploitation of Meteorological Satellites Multisensor Precipitation Estimate program.

关键词： Data mining MSG images apriori algorithm fuzzy association rules fuzzy c-means algorithm

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Vector quantization of images with codeword-rotation algorithm

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JOURNAL OF THE cHINESE INSTITUTE OF ENGINEERS 1998年第4期21卷 389-397页

作者： chiu, cc Hsieh, cH Chung Cheng Inst Technol Dept Elect Engn Tao Yuan 335 Taiwan I Shoh Univ Dept Informat Engn Kaohsiung 840 Taiwan

A codeword-rotation algorithm is proposed for vector quantization (VQ) of images. A novel binary classifier is presented to preclassify the training vectors into six classes including edge blocks and nonedge blocks. The VQ codebook is generated by applying the modified fuzzy c-means (MFcM) algorithm to the training vectors of each class. Similar edge blocks are rotated and coalesced during the edge subcodebook generation process. Furthermore, two schemes for designing the encoder and decoder are also presented. compared with the basic VQ system constructed by the LEG algorithm, the new method results in a considerable reduction in codebook size and computation time of codebook generation. More importantly, the visual quality achieved is better than the basic VQ system.

关键词： vector quantization fuzzy c-means algorithm LEG algorithm

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Predicting stain repellency characteristics of knitted fabrics using fuzzy modeling and surface response methodology

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JOURNAL OF THE TEXTILE INSTITUTE 2017年第5期108卷 683-691页

作者： Kabbari, Monia Fayala, Faten Ghith, Adel Liouane, Noureddine Monastir Natl Sch Engineers Dept Text Monastir Tunisia LARA TSI Lab Rech Automat Traitement Signal & Ima Monastir Tunisia Univ Monastir LESTE Monastir Tunisia

The effect of a stain repellent treatment on the water-oil repellency characteristics of plush knitted fabrics is investigated. We compared the efficiency of two methods of modeling;a Multicriteria analysis was employed by means of surface response method and an artificial intelligence-based system approach is presented by fuzzy logic modeling in which the effects of the operating parameters and intrinsic features of fabrics are studied. These parameters were pre-selected according to their possible influence on the outputs which were the contact angle and the air permeability. An original fuzzy logic-based method was proposed to select the most relevant parameters. The results show that air permeability was influenced essentially by knitted structure's parameters but the variation of treatment parameters has a great effect on the contact angle. Thus, it is believed that artificial intelligence system could efficiently be applied to the knit industry to understand, evaluate, and predict water-oil repellency parameters of plush knitted fabrics more than Multicriteria analysis.

关键词： Air permeability fuzzy c-means algorithm fuzzy logic plush knit surface response water-oil repellency

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Automatic recognition of the color effect of yarn-dyed fabric by the smallest repeat unit recognition algorithm

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TEXTILE RESEARcH JOURNAL 2015年第4期85卷 432-446页

作者： Zhang, Jie Pan, Ruru Gao, Weidong Zhu, Dandan Jiangnan Univ Sch Text & Clothing Wuxi 214122 Peoples R China

In this paper, an effective method based on the smallest repeat unit recognition (SRUR) algorithm is proposed to inspect the color effect of yarn-dyed fabric automatically. This method consists of three main steps: (1) color pattern preliminary recognition;(2) weave repeat unit recognition;(3) color yarn repeat unit recognition. In the first step, the floats in the fabric are located by yarn position segmented with mathematical statistics of sub-images and the colors of all floats classified by the fuzzy c-means algorithm. The color yarn layout is recognized by statistical analysis and the color pattern is roughly generated. In the second step, the weave repeat unit is found based on the preliminary color pattern. The weave repeat unit is extracted from the incompletely recognized weave pattern matrix by the SRUR algorithm. In the last step, according to the weave repeat unit and the preliminary identified color pattern, the color yarn layout is rectified by the improved statistical analysis, and the color yarn repeat unit is finally obtained by the SRUR algorithm. According to the weave and color yarn repeat units, the color effect is produced. The experimental analysis proved that the proposed method can recognize color effects of yarn-dyed fabrics with satisfactory accuracy.

关键词： yarn-dyed fabric color effect weave pattern smallest repeat unit recognition algorithm fuzzy c-means algorithm

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An efficient approach for unsupervised fuzzy clustering based on grouping evolution strategies

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PATTERN REcOGNITION 2013年第5期46卷 1240-1254页

作者： Kashan, Ali Husseinzadeh Rezaee, Babak Karimiyan, Somayyeh Islamic Azad Univ Fac Engn Dept Syst & Ind Engn Sci & Res Branch Tehran Iran Bojnord Univ Dept Ind Engn Bojnord Iran Int Inst Earthquake Engn & Seismol Dept Earthquake Engn Tehran Iran

fuzzy data clustering plays an important role in practical use and has become a prerequisite step for decision-making in fuzzy environment. In this paper we propose a new algorithm, called fuzzyGES for unsupervised fuzzy clustering based on adaptation of the recently proposed Grouping Evolution Strategy (GES). To adapt GES for fuzzy clustering we devise a fuzzy counterpart of the grouping mutation operator typically used in GES, and employ it in a two phase procedure to generate a new clustering solution. Unlike conventional clustering algorithms which should get the number of clusters as an input, fuzzyGES tries to determine the true number of clusters as well as providing the optimal cluster centroids after several iterations. The proposed approach is validated through using several data sets and results are compared with those of fuzzy c-means algorithm, particle swarm optimization algorithm (PSO), differential evolution (DE) and league championship algorithm (LcA). We also investigate the performance of fuzzyGES through using different cluster validity indices. Our results indicate that fuzzyGES is fast and provides acceptable results in terms of both determining the correct number of clusters and the accurate cluster centroids. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： clustering fuzzy Grouping evolution strategy fuzzy c-means algorithm

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A novel parameter estimation in dynamic model via fuzzy swarm intelligence and chaos theory for faults in wastewater treatment plant

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SOFT cOMPUTING 2020年第1期24卷 111-129页

作者： Anter, Ahmed M. Gupta, Deepak castillo, Oscar Beni Suef Univ Fac Comp & Informat Bani Suwayf 62511 Egypt Maharaja Agrsen Inst Technol Delhi India Tijuana Inst Technol Tijuana Mexico

Faults during a wastewater treatment for plant (WWTP) are critical issue for social and biological. Poorly treated wastewater may achieve dangerous effect for human as well as nature. This paper proposed a novel model based on a binary version of whale optimization algorithm (WOA), chaos theory and fuzzy logic, namely (cF-BWOA). cF-BWOA is applied in the application of WWTP to find out the more relevant attributes from the whole dataset, reducing cost and validation of decision rules, and helping to identify a non-well-structured domain. cF-BWOA attempts to reduce the whole feature set without loss of significant information to the classification process. Fast fuzzy c-means is used as a cost function to measure the fuzzification and uncertainty of data. Ten different chaos sequence maps are used to estimate and tune WOA parameters. Experiments are applied on a complex real-time dataset with various uncertainty features and missing values. The overall result indicates that the cWOA with the Sine chaos map shows the better performance, lower error, higher convergence speed and shorter execution time. In addition, the proposed model is capable of detecting sensor process faults in WWTP with high accuracy and can guide the operators of these systems to control decisions.

关键词： Fault detection Whale optimization algorithm Wastewater treatment chaos theory fuzzy c-means algorithm

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Energy saving and prediction modeling of petrochemical industries: A novel ELM based on FAHP

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ENERGY 2017年 122卷 350-362页

作者： Geng, ZhiQiang Qin, Lin Han, YongMing Zhu, QunXiong Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China Minist Educ China Engn Res Ctr Intelligent PSE Beijing 100029 Peoples R China

Extreme learning machine (ELM), which is a simple single-hidden-layer feed-forward neural network with fast implementation, has been widely applied in many engineering fields. However, it is difficult to enhance the modeling ability of extreme learning in disposing the high-dimensional noisy data. And the predictive modeling method based on the ELM integrated fuzzy c-means integrating analytic hierarchy process (FAHP) (FAHP-ELM) is proposed. The fuzzy c-means algorithm is used to cluster the input attributes of the high-dimensional data. The Analytic Hierarchy Process (AHP) based on the entropy weights is proposed to filter the redundant information and extracts characteristic components. Then, the fusion data is used as the input of the ELM. compared with the back-propagation (BP) neural network and the ELM, the proposed model has better performance in terms of the speed of convergence, generalization and modeling accuracy based on University of california Irvine (UcI) benchmark datasets. Finally, the proposed method was applied to build the energy saving and predictive model of the purified terephthalic acid (PTA) solvent system and the ethylene production system. The experimental results demonstrated the validity of the proposed method. Meanwhile, it could enhance the efficiency of energy utilization and achieve energy conservation and emission reduction. (c) 2017 Elsevier Ltd. All rights reserved.

关键词： Extreme learning machine fuzzy c-means algorithm Analytic hierarchy process Energy conservation and emissions reduction Petrochemical industries

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A Large-Scale Group Decision-Making Method Fusing Three-Way clustering and Regret Theory Under fuzzy Preference Relations

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IEEE TRANSAcTIONS ON fuzzy SYSTEMS 2024年第9期32卷 4846-4860页

作者： Guo, Lun Zhan, Jianming Zhang, chao Xu, Zeshui Hubei Minzu Univ Sch Math & Stat Enshi 445000 Peoples R China Shanxi Univ Sch Comp & Informat Technol Key Lab Computat Intelligence & Chinese Informat P Minist Educ Taiyuan 030006 Shanxi Peoples R China Sichuan Univ Sch Business Chengdu Sichuan Peoples R China

computational intelligence is increasingly applied to complex decision-making challenges, leveraging its data analysis prowess. Hybrid human-artificial intelligence models enhance the grasp of intricate social behaviors, offering valuable insights for social computing and behavior modeling. Within this landscape, large-scale group decision-making (LSGDM) emerges as an invaluable asset for navigating intricate decision-making scenarios. LSGDM enlists the expertise of individuals who articulate their preferences via fuzzy preference relations that abide by additively consistent principles. Its ascendancy is underscored by its applicability and relevance in confronting multifarious decision-making conundrums. In the realm of LSGDM, machine learning methodologies, such as cluster analysis, are deployed to streamline decision-making procedures, particularly when confronted with inherent complexities. The consensus reaching process (cRP) serves as the cornerstone, ensuring that decision makers (DMs) converge on a unified verdict. consequently, comprehensive exploration of cluster analysis and cRP assumes a pivotal role in elevating the effectiveness of LSGDM. To further augment LSGDM, this study leverages a three-way clustering approach grounded in adaptive fuzzy c-mean clustering. This stratagem categorizes DMs into discrete subgroups. Moreover, a consensus metric, embracing both cardinal and ordinal consensus considerations, is established. This metric serves as the foundation for computing DMs' intragroup weights and group weights. Moreover, this article introduces a feedback mechanism imbued with identification and modification rules (directional rules). It incorporates a modification function that takes into account the consensus threshold, DMs' regret psychology, and the consensus level. This modification function methodically derives modification parameters for the spectrum of DMs. Finally, the viability and effectiveness of the LSGDM methodology proffered in this

关键词： consensus reaching process (cRP) fuzzy preference relation (FPR) large-scale group decision-making (LSGDM) three-way clustering (TWc) consensus reaching process (cRP) fuzzy c-means algorithm fuzzy c-means algorithm large-scale group decision-making (LSGDM) three-way clustering (TWc)

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