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

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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fuzzy clustering based on K-nearest-neighbours rule

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fuzzy SETS AND SYSTEMS 2001年第2期120卷 239-247页

作者： Zahid, N Abouelala, O Limouri, M Essaid, A Univ Mohammed V AGDAL Fac Sci Lab Concept & Syst Rabat Morocco

In cluster analysis, several algorithms have been made for partitioning a set of objects into c natural clusters. in general, this problem is formulated as being an objective function optimization approach. However, it is known that the function being minimized is nonconvex and hence it may lead to convergence to many local minima, i.e., to different partitions. Thus, the clustering is repeated with different initializations hoping that some runs will lead to the global minimum. Therefore, the performance of these algorithms depends largely on good choice of these initializations. The most widely used algorithm using this function is called fuzzy c-means algorithm (FcMA). Zn this paper a new algorithm is proposed to carry out fuzzy clustering without a priori assumptions on initial guesses. This algorithm is based on two-layer clustering strategy. During the first layer, the K-nearest-neighbours decision rule is used. Then, to achieve an optimal partition, the second layer involves one iteration of FcMA. The performance of the proposed algorithm and that of FcMA have been tested on six data sets. The results obtained show that the new algorithm possesses a number of advantages over the FcMA. (c) 2001 Elsevier Science B.V. All rights reserved.

关键词： cluster analysis fuzzy c-means algorithm K-nearest-neighbours decision rule fuzzy sets pattern recognition

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fuzzy block truncation coding

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OPTIcAL ENGINEERING 2002年第12期41卷 3161-3167页

作者： Boudraa, AO Beghdadi, A Dehalk, SMR Iordache, R Ecole Navale IRENav F-29200 Brest France Univ Paris 13 F-93430 Villetaneuse France ENST Dept Image F-75634 Paris France Tampere Univ Technol Inst Signal Proc FIN-33101 Tampere Finland

Block truncation coding (BTc) is a well known lossy compression scheme. Due to its low complexity and easy implementation, BTc has gained wide interest in its further development and application for image compression. Based on simple thresholding, BTc retains sharp edges and thus leads to artifacts such as the staircase effect. The second problem encountered in BTc is the splitting of homogeneous regions, which produces false contours. In this work a fuzzy approach of BTc to avoid truncating homogeneous blocks and to preserve smooth edges in two-cluster blocks is proposed. Each image block, viewed as a fuzzy set, is segmented into two clusters using a fuzzy clustering algorithm. The block is then encoded by modified fuzzy weighted means of the two clusters. Initialization strategies of the fuzzy clustering algorithm and a contextual quantization method are proposed. Experimental results show an improvement of visual quality of reconstructed images and peak signal-to-noise ratio when compared to BTc, economical BTc (EBTc), absolute moment BTc (AMBTc), and a minimum mean square error quantizer (MMSEQ). computation time required by AMBTc, EBTc, and fuzzy BTc methods are reported. (c) 2002 Society of Photo-Optical Instrumentation Engineers.

关键词： image processing image coding block truncation coding fuzzy clustering fuzzy c-means algorithm contextual quantization

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3D reconstruction of structural magnetic resonance neuroimaging based on computer aided detection

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INTERNATIONAL JOURNAL OF BIO-INSPIRED cOMPUTATION 2021年第3期17卷 174-181页

作者： Mabrouk, Mai S. Afify, Heba M. Marzouk, Samir Y. Misr Univ Sci & Technol MUST Univ Biomed Engn Giza Egypt Higher Inst Engn El Shorouk City Syst & Biomed Engn Dept Cairo Egypt Arab Acad Sci & Technol Dept Basic & Appl Sci Giza Egypt

computer aided detection (cAD) has numerous achievements in medical image processing field which represents a communication. Despite the advance in the radiological diagnosis of neuroimaging, magnetic resonance imaging (MRI) has some restrictions related to human errors and incomplete interpretation of brain tumour regions. Also, MRI produces 2D images of the brain that are very difficult to handle different types of tumour. This study investigated cAD system using 3D image reconstruction of MR brain and tumour structures efficiently. In addition, the proposed system applied the fuzzy c-means (FcM) algorithm as image segmentation, and support vector machine (SVM) as image classification for tumour detection of MR brain images. The results confirmed that this 3D model supported the advanced view of human brain diseases. The accurate and quantitative research results are expected to enhance the healthcare technology.

关键词： computer aided detection cAD magnetic resonance imaging MRI 3D image reconstruction support vector machine SVM fuzzy c-means algorithm fuzzy c-means FcM

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Robust unsupervised small area change detection from SAR imagery using deep learning

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2021年 173卷 79-94页

作者： Zhang, Xinzheng Su, Hang Zhang, ce Gu, Xiaowei Tan, Xiaoheng Atkinson, Peter M. Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Chongqing Key Lab Space Informat Network & Intell Chongqing 400044 Peoples R China Univ Lancaster Lancaster Environm Ctr Lancaster LA1 4YQ England UK Ctr Ecol & Hydrol Lib Ave Lancaster LA1 4AP England Aberystwyth Univ Dept Comp Sci Aberystwyth SY23 3DB Dyfed Wales Univ Southampton Geog & Environm Sci Southampton SO17 1BJ Hants England Chinese Acad Sci Inst Geog Sci & Nat Resources Res 11A Datun Rd Beijing 100101 Peoples R China

Small area change detection using synthetic aperture radar (SAR) imagery is a highly challenging task, due to speckle noise and imbalance between classes (changed and unchanged). In this paper, a robust unsupervised approach is proposed for small area change detection using deep learning techniques. First, a multi-scale superpixel reconstruction method is developed to generate a difference image (DI), which can suppress the speckle noise effectively and enhance edges by exploiting local, spatially homogeneous information. Second, a two-stage centre-constrained fuzzy c-means clustering algorithm is proposed to divide the pixels of the DI into changed, unchanged and intermediate classes with a parallel clustering strategy. Image patches belonging to the first two classes are then constructed as pseudo-label training samples, and image patches of the intermediate class are treated as testing samples. Finally, a convolutional wavelet neural network (cWNN) is designed and trained to classify testing samples into changed or unchanged classes, coupled with a deep convolutional generative adversarial network (DcGAN) to increase the number of changed class within the pseudo-label training samples. Numerical experiments on four real SAR datasets demonstrate the validity and robustness of the proposed approach, achieving up to 99.61% accuracy for small area change detection.

关键词： change detection Synthetic aperture radar Difference image fuzzy c-means algorithm Deep learning

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Genetic fuzzy entropy-constrained vector quantization

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JOURNAL OF THE cHINESE INSTITUTE OF ENGINEERS 2001年第3期24卷 369-377页

作者： Hwang, WJ chine, cF Hong, SL Chung Yuan Christian Univ Dept Elect Engn Chungli 320 Taiwan

A novel variable-rare vector quantizer (VQ) design algorithm using both generic and fuzzy clustering techniques is presented. The algorithm, termed genetic fuzzy entropy-constrained VQ (GFEcVQ) design algorithm, has a superior rate-distortion performance than that of the existing variable-rate VQ design algorithms. The algorithm utilizes fuzzy clustering technique to enhance the rate-distortion performance for the VQ design. In addition, a novel genetic algorithm is employed to ensure the robustness of the performance against the selection of initial parameters. Simulation results demonstrate that the FEcVQ can be an effective alternative for the design of variable-rate VQs.

关键词： fuzzy clustering vector quantization image compression fuzzy c-means algorithm

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On fuzzy cluster validity indices

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fuzzy SETS AND SYSTEMS 2007年第19期158卷 2095-2117页

作者： Wang, Weina Zhang, Yunjie Dalian Maritime Univ Dept Math Dalian Liaoning Prov Peoples R China Jilin Inst Chem Technol Dept Math Jilin Jilin Province Peoples R China

cluster analysis aims at identifying groups of similar objects, and helps to discover distribution of patterns and interesting correlations in large data sets. Especially, fuzzy clustering has been widely studied and applied in a variety of key areas and fuzzy cluster validation plays a very important role in fuzzy clustering. This paper introduces the fundamental concepts of cluster validity, and presents a review of fuzzy cluster validity indices available in the literature. We conducted extensive comparisons of the mentioned indices in conjunction with the fuzzy c-means clustering algorithm on a number of widely used data sets, and make a simple analysis of the experimental results. (c) 2007 Elsevier B.V All rights reserved.

关键词： fuzzy clustering cluster validity indices fuzzy c-means algorithm

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Validity index for crisp and fuzzy clusters

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PATTERN REcOGNITION 2004年第3期37卷 487-501页

作者： Pakhira, MK Bandyopadhyay, S Maulik, U Kalyani Govt Engn Coll Dept Comp Sci & Technol Kalyani 741235 W Bengal India Indian Stat Inst Machine Intelligence Unit Kolkata 700108 W Bengal India Kalyani Govt Engn Coll Dept Comp Sci & Technol Kalyani 741235 W Bengal India

In this article, a cluster validity index and its fuzzification is described, which can provide a measure of goodness of clustering on different partitions of a data set. The maximum value of this index, called the PBM-index, across the hierarchy provides the best partitioning. The index is defined as a product of three factors, maximization of which ensures the formation of a small number of compact clusters with large separation between at least two clusters. We have used both the k-means and the expectation maximization algorithms as underlying crisp clustering techniques. For fuzzy clustering, we have utilized the well-known fuzzy c-means algorithm. Results demonstrating the superiority of the PBM-index in appropriately determining the number of clusters, as compared to three other well-known measures, the Davies-Bouldin index, Dunn's index and the Xie-Beni index, are provided for several artificial and real-life data sets. (c) 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

关键词： clustering expectation maximization algorithm fuzzy c-means algorithm k-means algorithm unsupervised classification validity index

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chaotic time series prediction based on selection of important input variables

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JOURNAL OF INTELLIGENT & fuzzy SYSTEMS 2022年第6期43卷 8289-8301页

作者： Ren, Yaxue Wen, Yintang Liu, Fucai Zhang, Yuyan Yanshan Univ Key Lab Measurement Technol & Instrumentat Hebei Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao Hebei Peoples R China

chaotic systems are dynamic systems with aperiodic and pseudo-random properties, and systems in many fields exhibit chaotic time-series properties. Aiming at the fuzzy modeling problem of chaotic time series, this paper proposes a new fuzzy identification method considering the selection of important input variables. The purpose is to achieve higher model modeling and prediction accuracy by constructing a model with a simple structure. The relevant input variable was swiftly chosen in accordance with the input variable index after the Two Stage fuzzy curves method was used to determine the weight of the correlation between each input variable and the output from a large number of selectable input variables. The center and width of the irregular Gaussian membership function were then optimized using the fuzzy c-means clustering algorithm and the particle swarm optimization technique, which led to the determination of the fuzzy model's underlying premise parameters. Finally, the fuzzy model's conclusion parameters were determined using the recursive least squares method. This model is used to simulate three chaotic time series, and the outcomes of the simulation are contrasted and examined. The outcomes demonstrate that the fuzzy identification system has higher prediction accuracy based on a simpler structure, demonstrating its validity.

关键词： fuzzy identification input variable selection chaotic time series fuzzy c-means algorithm irregular gaussian function

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