Bibliographies thématiques / FUZZY TECHNIQUE

Littérature scientifique sur le sujet « FUZZY TECHNIQUE »

Auteur : Grafiati
Publié le 11 septembre 2023

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Articles de revues sur le sujet "FUZZY TECHNIQUE"

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Dey, Aniruddha, Jamuna Kanta Sing et Shiladitya Chowdhury. « Weighted Fuzzy Generalized 2DFLD : A Fuzzy-Based Feature Extraction Technique for Face Recognition ». International Journal of Machine Learning and Computing 7, no 6 (décembre 2017) : 223–31. http://dx.doi.org/10.18178/ijmlc.2017.7.6.651.

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Ansari, Mohd Zeeshan, et M. M. Sufyan Beg. « Improved Fuzzy Rank Aggregation ». International Journal of Rough Sets and Data Analysis 5, no 4 (octobre 2018) : 74–87. http://dx.doi.org/10.4018/ijrsda.2018100105.

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Rank aggregation is applied on the web to build various applications like meta-search engines, consumer reviews classification, and recommender systems. Meta-searching is the generation of a single list from a collection of the results produced by multiple search engines, together using a rank aggregation technique. It is an efficient and cost-effective technique to retrieve quality results from the internet. The quality of results produced by a meta-searching relies upon the efficiency of rank aggregation technique applied. An effective rank aggregation technique assigns the rank to a document that is closest to all its previous rankings. The newly generated list of documents may be evaluated by the measurement of Spearman footrule distance. In this article, various fuzzy logic techniques for rank aggregation are analyzed and further improvements are proposed in Modified Shimura technique. Consequently, two novel OWA operators are suggested for the calculation of membership values of document ranks in a modified Shimura technique. The performance of proposed improvements is evaluated on the Spearman footrule distance along with execution time. The results show that the anticipated improvements exhibit better performance than other fuzzy techniques.
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Zhai, Jun-hai. « Fuzzy decision tree based on fuzzy-rough technique ». Soft Computing 15, no 6 (10 mars 2010) : 1087–96. http://dx.doi.org/10.1007/s00500-010-0584-0.

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S, Pradeep. « Design and FPGA Implementation of Image Compression Based Fuzzy Technique ». International Journal of Electrical and Electronics Research 2, no 2 (30 septembre 2014) : 1–4. http://dx.doi.org/10.37391/ijeer.020201.

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Fuzzy logic is a way to embed an engineer’s experience into the system. In recent years, many researchers have applied the fuzzy logic to develop new techniques for contrast improvement. Fuzzy logic is a well-known rather simple approach with good visual results, but proposed fuzzy operation algorithm is default nonlinear. Here proposed algorithm is a default nonlinear thus not straight forward applicable on the JPEG bit stream, it is possible when the right combination is found. Image compression is one of the major image processing techniques that are widely used in medical, automotive, consumer and military applications. In this project fuzzy technique has been used in image compression with Discrete Wavelet Transforms (DWT) technique. Discrete wavelet transforms is the most popular transformation technique adopted for image compression. Image compression has become important as storage or transmission of images requires large amount of bandwidth. In order to minimize the complexity of DWT, fuzzy technique has been proposed and implemented on FPGA.
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Ahmad, Mohammad, et Weihu Cheng. « A Novel Approach of Fuzzy Control Chart with Fuzzy Process Capability Indices Using Alpha Cut Triangular Fuzzy Number ». Mathematics 10, no 19 (30 septembre 2022) : 3572. http://dx.doi.org/10.3390/math10193572.

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Statistical Quality Control is a valuable strategy that applies to the statistical technique for monitoring a manufacturing system under particular situations. On the other hand, the fuzzy set theory is an ideal instrument to cope with an unclear situation. The existing studies are restricted, and there is still mystery behind the unclear data. This paper deals with technique: namely, the fuzzy control chart based on fuzzy process capability indices (FCPI) using triangular fuzzy numbers (TFNs). Alpha cut theory is applied in statistical quality control for fuzzy process control industrial application. This is a five-phase study that deals with the control chart using capability indices. The numerical example is also performed using the proposed technique. This paper would help to better assess/understand the manufacturing system data and would explore the application of the fuzzy control techniques.
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Das, Satya Kumar, et Sahidul Islam. « Multi-item a supply chain production inventory model of time dependent production rate and demand rate under space constraint in fuzzy environment ». Independent Journal of Management & ; Production 11, no 2 (1 avril 2020) : 304. http://dx.doi.org/10.14807/ijmp.v11i2.1037.

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In this paper, we have developed an integrated production inventory model for two echelon supply chain consisting of one vendor and one retailer. Production rate and demand rate of retailer and customer are time dependent. Idle time cost of the vendor has been considered. Multi-item inventory has been considered. In integrated inventory model average cost has been calculated under limitation on stroge space. Two echelon supply chain fuzzy inventory model has been solved by various techniques like as Fuzzy programming technique with hyperbolic membership functions (FPTHMF), Fuzzy non-linear programming technique (FNLP) and Fuzzy additive goal programming technique (FAGP), weighted Fuzzy non-linear programming technique (WFNLP) and weighted Fuzzy additive goal programming technique (WFAGP). A numerical example is illustrated to test the model. Finally to make the model more realistic, sensitivity analysis has been shown.
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Taleshian, Fatemeh, et Jafar Fathali. « A Mathematical Model for Fuzzyp-Median Problem with Fuzzy Weights and Variables ». Advances in Operations Research 2016 (2016) : 1–13. http://dx.doi.org/10.1155/2016/7590492.

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We investigate thep-median problem with fuzzy variables and weights of vertices. The fuzzy equalities and inequalities transform to crisp cases by using some technique used in fuzzy linear programming. We show that the fuzzy objective function also can be replaced by crisp functions. Therefore an auxiliary linear programming model is obtained for the fuzzyp-median problem. The results are compared with two previously proposed methods.
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M. Mousa, Hamdy. « Chaotic Genetic-fuzzy Encryption Technique ». International Journal of Computer Network and Information Security 10, no 4 (8 avril 2018) : 10–19. http://dx.doi.org/10.5815/ijcnis.2018.04.02.

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Sinha, S. B., K. A. Rao, B. K. Mangaraj et P. K. Tripathy. « Fuzzy Technique to Agricultural Planning ». Journal of Information and Optimization Sciences 10, no 1 (janvier 1989) : 257–74. http://dx.doi.org/10.1080/02522667.1989.10698965.

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Cai, Yuliang, Huaguang Zhang, Qiang He et Shaoxin Sun. « New classification technique : fuzzy oblique decision tree ». Transactions of the Institute of Measurement and Control 41, no 8 (11 juin 2018) : 2185–95. http://dx.doi.org/10.1177/0142331218774614.

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Based on axiomatic fuzzy set (AFS) theory and fuzzy information entropy, a novel fuzzy oblique decision tree (FODT) algorithm is proposed in this paper. Traditional axis-parallel decision trees only consider a single feature at each non-leaf node, while oblique decision trees partition the feature space with an oblique hyperplane. By contrast, the FODT takes dynamic mining fuzzy rules as a decision function. The main idea of the FODT is to use these fuzzy rules to construct leaf nodes for each class in each layer of the tree; the samples that cannot be covered by the fuzzy rules are then put into an additional node – the only non-leaf node in this layer. Construction of the FODT consists of four major steps: (a) generation of fuzzy membership functions automatically by AFS theory according to the raw data distribution; (b) extraction of dynamically fuzzy rules in each non-leaf node by the fuzzy rule extraction algorithm (FREA); (c) construction of the FODT by the fuzzy rules obtained from step (b); and (d) determination of the optimal threshold [Formula: see text] to generate a final tree. Compared with five traditional decision trees (C4.5, LADtree (LAD), Best-first tree (BFT), SimpleCart (SC) and NBTree (NBT)) and a recently obtained fuzzy rules decision tree (FRDT) on eight UCI machine learning data sets and one biomedical data set (ALLAML), the experimental results demonstrate that the proposed algorithm outperforms the other decision trees in both classification accuracy and tree size.
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Thèses sur le sujet "FUZZY TECHNIQUE"

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Aranibar, Luis Alfonso Quiroga. « Learning fuzzy logic from examples ». Ohio : Ohio University, 1994. http://www.ohiolink.edu/etd/view.cgi?ohiou1176495652.

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Zhao, Mansuo. « Image Thresholding Technique Based On Fuzzy Partition And Entropy Maximization ». University of Sydney. School of Electrical and Information Engineering, 2005. http://hdl.handle.net/2123/699.

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Thresholding is a commonly used technique in image segmentation because of its fast and easy application. For this reason threshold selection is an important issue. There are two general approaches to threshold selection. One approach is based on the histogram of the image while the other is based on the gray scale information located in the local small areas. The histogram of an image contains some statistical data of the grayscale or color ingredients. In this thesis, an adaptive logical thresholding method is proposed for the binarization of blueprint images first. The new method exploits the geometric features of blueprint images. This is implemented by utilizing a robust windows operation, which is based on the assumption that the objects have "e;C"e; shape in a small area. We make use of multiple window sizes in the windows operation. This not only reduces computation time but also separates effectively thin lines from wide lines. Our method can automatically determine the threshold of images. Experiments show that our method is effective for blueprint images and achieves good results over a wide range of images. Second, the fuzzy set theory, along with probability partition and maximum entropy theory, is explored to compute the threshold based on the histogram of the image. Fuzzy set theory has been widely used in many fields where the ambiguous phenomena exist since it was proposed by Zadeh in 1965. And many thresholding methods have also been developed by using this theory. The concept we are using here is called fuzzy partition. Fuzzy partition means that a histogram is parted into several groups by some fuzzy sets which represent the fuzzy membership of each group because our method is based on histogram of the image . Probability partition is associated with fuzzy partition. The probability distribution of each group is derived from the fuzzy partition. Entropy which originates from thermodynamic theory is introduced into communications theory as a commonly used criteria to measure the information transmitted through a channel. It is adopted by image processing as a measurement of the information contained in the processed images. Thus it is applied in our method as a criterion for selecting the optimal fuzzy sets which partition the histogram. To find the threshold, the histogram of the image is partitioned by fuzzy sets which satisfy a certain entropy restriction. The search for the best possible fuzzy sets becomes an important issue. There is no efficient method for the searching procedure. Therefore, expansion to multiple level thresholding with fuzzy partition becomes extremely time consuming or even impossible. In this thesis, the relationship between a probability partition (PP) and a fuzzy C-partition (FP) is studied. This relationship and the entropy approach are used to derive a thresholding technique to select the optimal fuzzy C-partition. The measure of the selection quality is the entropy function defined by the PP and FP. A necessary condition of the entropy function arriving at a maximum is derived. Based on this condition, an efficient search procedure for two-level thresholding is derived, which makes the search so efficient that extension to multilevel thresholding becomes possible. A novel fuzzy membership function is proposed in three-level thresholding which produces a better result because a new relationship among the fuzzy membership functions is presented. This new relationship gives more flexibility in the search for the optimal fuzzy sets, although it also increases the complication in the search for the fuzzy sets in multi-level thresholding. This complication is solved by a new method called the "e;Onion-Peeling"e; method. Because the relationship between the fuzzy membership functions is so complicated it is impossible to obtain the membership functions all at once. The search procedure is decomposed into several layers of three-level partitions except for the last layer which may be a two-level one. So the big problem is simplified to three-level partitions such that we can obtain the two outmost membership functions without worrying too much about the complicated intersections among the membership functions. The method is further revised for images with a dominant area of background or an object which affects the appearance of the histogram of the image. The histogram is the basis of our method as well as of many other methods. A "e;bad"e; shape of the histogram will result in a bad thresholded image. A quadtree scheme is adopted to decompose the image into homogeneous areas and heterogeneous areas. And a multi-resolution thresholding method based on quadtree and fuzzy partition is then devised to deal with these images. Extension of fuzzy partition methods to color images is also examined. An adaptive thresholding method for color images based on fuzzy partition is proposed which can determine the number of thresholding levels automatically. This thesis concludes that the "e;C"e; shape assumption and varying sizes of windows for windows operation contribute to a better segmentation of the blueprint images. The efficient search procedure for the optimal fuzzy sets in the fuzzy-2 partition of the histogram of the image accelerates the process so much that it enables the extension of it to multilevel thresholding. In three-level fuzzy partition the new relationship presentation among the three fuzzy membership functions makes more sense than the conventional assumption and, as a result, performs better. A novel method, the "e;Onion-Peeling"e; method, is devised for dealing with the complexity at the intersection among the multiple membership functions in the multilevel fuzzy partition. It decomposes the multilevel partition into the fuzzy-3 partitions and the fuzzy-2 partitions by transposing the partition space in the histogram. Thus it is efficient in multilevel thresholding. A multi-resolution method which applies the quadtree scheme to distinguish the heterogeneous areas from the homogeneous areas is designed for the images with large homogeneous areas which usually distorts the histogram of the image. The new histogram based on only the heterogeneous area is adopted for partition and outperforms the old one. While validity checks filter out the fragmented points which are only a small portion of the whole image. Thus it gives good thresholded images for human face images.
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Zhao, Mansuo. « Image Thresholding Technique Based On Fuzzy Partition And Entropy Maximization ». Thesis, The University of Sydney, 2004. http://hdl.handle.net/2123/699.

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Thresholding is a commonly used technique in image segmentation because of its fast and easy application. For this reason threshold selection is an important issue. There are two general approaches to threshold selection. One approach is based on the histogram of the image while the other is based on the gray scale information located in the local small areas. The histogram of an image contains some statistical data of the grayscale or color ingredients. In this thesis, an adaptive logical thresholding method is proposed for the binarization of blueprint images first. The new method exploits the geometric features of blueprint images. This is implemented by utilizing a robust windows operation, which is based on the assumption that the objects have "e;C"e; shape in a small area. We make use of multiple window sizes in the windows operation. This not only reduces computation time but also separates effectively thin lines from wide lines. Our method can automatically determine the threshold of images. Experiments show that our method is effective for blueprint images and achieves good results over a wide range of images. Second, the fuzzy set theory, along with probability partition and maximum entropy theory, is explored to compute the threshold based on the histogram of the image. Fuzzy set theory has been widely used in many fields where the ambiguous phenomena exist since it was proposed by Zadeh in 1965. And many thresholding methods have also been developed by using this theory. The concept we are using here is called fuzzy partition. Fuzzy partition means that a histogram is parted into several groups by some fuzzy sets which represent the fuzzy membership of each group because our method is based on histogram of the image . Probability partition is associated with fuzzy partition. The probability distribution of each group is derived from the fuzzy partition. Entropy which originates from thermodynamic theory is introduced into communications theory as a commonly used criteria to measure the information transmitted through a channel. It is adopted by image processing as a measurement of the information contained in the processed images. Thus it is applied in our method as a criterion for selecting the optimal fuzzy sets which partition the histogram. To find the threshold, the histogram of the image is partitioned by fuzzy sets which satisfy a certain entropy restriction. The search for the best possible fuzzy sets becomes an important issue. There is no efficient method for the searching procedure. Therefore, expansion to multiple level thresholding with fuzzy partition becomes extremely time consuming or even impossible. In this thesis, the relationship between a probability partition (PP) and a fuzzy C-partition (FP) is studied. This relationship and the entropy approach are used to derive a thresholding technique to select the optimal fuzzy C-partition. The measure of the selection quality is the entropy function defined by the PP and FP. A necessary condition of the entropy function arriving at a maximum is derived. Based on this condition, an efficient search procedure for two-level thresholding is derived, which makes the search so efficient that extension to multilevel thresholding becomes possible. A novel fuzzy membership function is proposed in three-level thresholding which produces a better result because a new relationship among the fuzzy membership functions is presented. This new relationship gives more flexibility in the search for the optimal fuzzy sets, although it also increases the complication in the search for the fuzzy sets in multi-level thresholding. This complication is solved by a new method called the "e;Onion-Peeling"e; method. Because the relationship between the fuzzy membership functions is so complicated it is impossible to obtain the membership functions all at once. The search procedure is decomposed into several layers of three-level partitions except for the last layer which may be a two-level one. So the big problem is simplified to three-level partitions such that we can obtain the two outmost membership functions without worrying too much about the complicated intersections among the membership functions. The method is further revised for images with a dominant area of background or an object which affects the appearance of the histogram of the image. The histogram is the basis of our method as well as of many other methods. A "e;bad"e; shape of the histogram will result in a bad thresholded image. A quadtree scheme is adopted to decompose the image into homogeneous areas and heterogeneous areas. And a multi-resolution thresholding method based on quadtree and fuzzy partition is then devised to deal with these images. Extension of fuzzy partition methods to color images is also examined. An adaptive thresholding method for color images based on fuzzy partition is proposed which can determine the number of thresholding levels automatically. This thesis concludes that the "e;C"e; shape assumption and varying sizes of windows for windows operation contribute to a better segmentation of the blueprint images. The efficient search procedure for the optimal fuzzy sets in the fuzzy-2 partition of the histogram of the image accelerates the process so much that it enables the extension of it to multilevel thresholding. In three-level fuzzy partition the new relationship presentation among the three fuzzy membership functions makes more sense than the conventional assumption and, as a result, performs better. A novel method, the "e;Onion-Peeling"e; method, is devised for dealing with the complexity at the intersection among the multiple membership functions in the multilevel fuzzy partition. It decomposes the multilevel partition into the fuzzy-3 partitions and the fuzzy-2 partitions by transposing the partition space in the histogram. Thus it is efficient in multilevel thresholding. A multi-resolution method which applies the quadtree scheme to distinguish the heterogeneous areas from the homogeneous areas is designed for the images with large homogeneous areas which usually distorts the histogram of the image. The new histogram based on only the heterogeneous area is adopted for partition and outperforms the old one. While validity checks filter out the fragmented points which are only a small portion of the whole image. Thus it gives good thresholded images for human face images.
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Muthu, Kavitha. « Expert system and fuzzy technique approaches to landslide hazard mapping ». Thesis, University of Surrey, 2005. http://epubs.surrey.ac.uk/722/.

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Yan, Yongyi. « Fuzzy modus ponens deduction technique for activity analysis in network planning ». Thesis, Queensland University of Technology, 1998. https://eprints.qut.edu.au/36075/1/36075_Yan_1998.pdf.

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A project is considered successful if it is completed within its scheduled duration and budgeted cost. To keep the project on schedule or budget, it is necessary for the project management to make an accurate forecast of project duration and cost to establish the pace of performance and also to revise it at regular intervals because of the dynamic environment of the project. This paper presents a new method, AADSS (Activity Analysis Decision Support System), to help the planner in estimating the duration and the cost of construction activities. This model employs the Fuzzy Modus Ponens Deduction (FMPD) technique to evaluate the impact of different factors on activity duration and cost. A computer program developed based on the AADSS does not require the planner to understand the underlying concept of fuzzy logic and it is simple to use. Thus, the model fulfills its objective in comprehensively simulating the dynamic uncertainty variables and their impact on activity duration and cost estimates so that a more reliable project completion forecast can be made.
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Zhao, Jibo. « An Efficient Wide-Speed Direct Torque Control Based on Fuzzy Logic Technique ». University of Toledo / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1352922315.

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7

Chen, Zhibin. « Segmentation of MRI images using non parametric deformable models integrating fuzzy technique ». Reims, 2009. http://theses.univ-reims.fr/exl-doc/GED00001122.pdf.

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L'objectif de la thèse est de développer une méthode automatique pour segmenter les tissus cérébraux (la matière grise, la matière blanche et le liquide céphalo-rachidien) à partir des images IRM, fournissant ainsi des mesures quantitatives et précises du cerveau. Dans cette thèse, nous avons développé trois modèles déformables non-paramétriques en intégrant l'information statistique et l’information floue des images pour segmenter le cerveau en différents types de tissus. Nous présentons d'abord une méthode basée sur l’analyse de l'histogramme. La répartition de l'intensité des images est modélisée par le modèle de mélanges gaussiens (MMG). Les paramètres du MMG sont estimés par l’algorithme «Expectation Maximization». Ensuite, ils sont utilisés pour guider l'évolution des courbes pour atteindre la segmentation des tissus cérébraux. Nous proposons ensuite une amélioration d’un algorithme basé sur les contours actifs orientés région avec la contrainte géométrique. Grâce à la nouvelle expression proposée, il permet de résoudre le problème de stabilité sous-jacente associé à l'algorithme d’origine, et réalise une convergence rapide. Enfin, nous présentons une segmentation de multi-classes en intégrant une segmentation floue dans la méthode level sets. Elle utilise un ensemble d'équations différentielles ordinaires. Chacune d'elles représente une classe à segmenter. Cette approche réduit la complexité de calcul par rapport à l'algorithme multi-phase existant, permettant donc d’accélérer la vitesse de convergence. Toutes les méthodes ont été évaluées avec des images IRM simulées et réelles. Les analyses quantitatives sont données. Les résultats sont très encourageants
The research goal of this thesis is to develop an automatic segmentation method to segment brain MRI images into different tissues (gray matter, white matter, and cerebrospinal fluid), providing quantitative and precise brain measurements. In this dissertation, we have developed three non-parametric deformable models integrating statistical information and fuzzy information of images to segment the brain into different tissue types from multi types of MRI images. We firstly present a histogram analysis based algorithm, where the intensity distribution of the MRI images is modeled via the mixture Gaussian model (MGM). The parameters of components in MGM are estimated via the Expectation Maximization (EM) algorithm. Then the estimated parameters are used to guide the evolution of the level set curves to achieve the brain tissue segmentation. We then propose an improved algorithm to region-based geometric active contour. Thanks to the new regional term, the new algorithm solves the underlying stability problem associated with the original algorithm, and achieves convergence with less iteration number compared with the original algorithm. Finally, we present a multiclass algorithm by integrating fuzzy segmentation with the level set methods. The algorithm uses a set of ordinary differential equations; each of them represents a class to be segmented. The multiclass algorithm reduces the computational complexity compared with the existing multiphase algorithm, so speeds up the convergence rate. All algorithms are evaluated with simulated and real MRI images, and quantitative analyses are provided. The results are very encouraging
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Casabayó, Bonàs Mònica. « Shopping behaviour forecasts : experiments based on a fuzzy learning technique in the Spanish food retailing industry ». Thesis, University of Edinburgh, 2005. http://hdl.handle.net/1842/24209.

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The general aim of this thesis is to analyse the possibility of developing synergies when connecting 3 different areas of research namely consumer behaviour, market research and artificial intelligence (AI). The three areas of research are very extensive. When analysing the potential links between them, a wide number of triple combinations arise. In addition, the number of combinations can also be increased when applied to different industries but the food retailing industry is selected as the framework of this thesis. A general overview of the three disciplines is developed. Firstly, consumer behaviour fundamentals are interpreted and reconsidered from a food retailer’s perspective. This constitutes one approach to the research in this thesis. Secondary, considering that learning from past data to anticipate shopping behaviours is a retailer’s focus of research, an overview of the main market research forecasting models and techniques is carried out. Thirdly, machine learning (AI subfield) is also explained in respect of its capability to perform forecasting tasks. Handling customer data is not easy. Information tends to be ambiguous, uncertain and incomplete. Moreover, the customer behaves differently according to his/her situation. Another AI subfield, fuzzy logic (Zadeh 1965) is also explained as it copes with the concept of partial truth. Having reviewed the three disciplines, the triple combination of ‘shoppers (household)’, ‘forecasting behaviours’ and ‘fuzzy learning’ aspects from each mentioned domain respectively are selected as illustrates the scope of this thesis. The empirical research consists of two experiments focused on forecasting shopper’s behaviour (in particular household shopping behaviour), in the food retailing industry using LAMDA (a fuzzy learning technique).The methodology of research is mainly based on data extracted from a Spanish Food Retailer’s (Supermarcats Pujol SA) databases. The first experiment is based on LAMDA’s supervised learning approach and provides a model to forecast the current customers who are going to defect when a competitor opens a supermarket in the same area. The second experiment is based on LAMDA’s unsupervised learning approach and provides a model to forecast the current customers who are going to buy online once the company launches the Website. Results indicate that marketing expert’s judgements are a key point when using learning techniques to forecast behaviours. Customers are not simple robots. People may change their behaviour according to their situation. The results show that when applying the adequacy degree (fuzzy logic concept), the forecasting accuracy increases considerably.
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Anderson, Alastair Andrew 1956. « The representation of personal constructs as fuzzy subsets : developing a model and testing its efficacy ». Monash University, Dept. of Management, 1999. http://arrow.monash.edu.au/hdl/1959.1/8592.

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Minotti, Cristiano. « Estimador fuzzy de velocidade para motores de indução trifásicos usando abordagem sensorless ». Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/18/18153/tde-15102008-135246/.

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O uso da tecnologia sensorless é uma tendência crescente para acionamentos industriais aplicados em máquinas elétricas. A estimação dos parâmetros elétricos e mecânicos envolvidos com o controle da máquina elétrica são utilizados freqüentemente para se evitar medir todas as variáveis envolvidas no processo. A redução de custos em acionamentos industriais, além do incremento da robustez do sistema, são algumas das vantagens do uso de técnicas sensorless. Este trabalho propõe o uso de lógica fuzzy para estimar a velocidade de rotação de motores de indução trifásicos. Estão presentes resultados de simulações computacionais e comparação com outras técnicas inteligentes para validação da abordagem apresentada.
The use of sensorless technologies is an increasing tendency on industrial drives for electrical machines. The estimation of electrical and mechanical parameters involved with the electric machine control is used very frequently in order to avoid measurement of all variables from this process. The cost reduction may also be considered in industrial drives, besides the increasing robustness of the system, as advantages of the use of sensorless technologies. This work proposes the use of fuzzy logic to estimate the speed in three-phase induction motors. Simulation results are presented to validate the proposed approach and comparative analyses with other intelligent techniques are also outlined.
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Livres sur le sujet "FUZZY TECHNIQUE"

1

Bryan, Debbie. Log Cabin Scribbles & Stitches presents Twigs, wings & fuzzy things. [Scott Depot, WV : Log Cabin Scribbles & Stitches, 2001.

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2

Wakefield, Pat. Fuzzy tales for little folk : 14 beautiful painting projects using your choice of tube oils or bottle acrylics. Norcross, GA (1649 International Blvd., P.O. Box 7600, Norcross 30091-7600) : Plaid Enterprises, 1991.

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Abdel-Kader, Magdy G. Investment decisions in advanced manufacturing technology : A fuzzy set theory approach. Brookfield, VT : Ashgate Pub., 1998.

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Dick, Scott, Vladik Kreinovich et Pawan Lingras, dir. Applications of Fuzzy Techniques. Cham : Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-16038-7.

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Kearfott, Ralph Baker, Ildar Batyrshin, Marek Reformat, Martine Ceberio et Vladik Kreinovich, dir. Fuzzy Techniques : Theory and Applications. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21920-8.

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Kerre, Etienne E., et Mike Nachtegael, dir. Fuzzy Techniques in Image Processing. Heidelberg : Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1847-5.

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Kandel, Abraham. Fuzzy mathematical techniques with applications. Reading, Mass : Addison-Wesley, 1986.

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E, Kerre Etienne, et Nachtegael Mike 1976-, dir. Fuzzy techniques in image processing. Heidelberg : Physica-Verlag, 2000.

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Fuzzy mathematical techniques with applications. Reading, Mass : Addison-Wesley, 1985.

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Islam, Sahidul, et Wasim Akram Mandal. Fuzzy Geometric Programming Techniques and Applications. Singapore : Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-5823-4.

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Chapitres de livres sur le sujet "FUZZY TECHNIQUE"

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Halide, Halmar, et Peter Ridd. « Modelling Coral Bleaching Events Using a Fuzzy Logic Technique ». Dans Fuzzy Logic, 352–62. Heidelberg : Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1806-2_26.

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Ghosh, Partha, et Krishna Kundu. « Proto-Fuzzy Concepts Generation Technique Using Fuzzy Graph ». Dans Springer Proceedings in Mathematics & ; Statistics, 63–74. New Delhi : Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2301-6_5.

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Liu, Hu-Chen. « FMEA Using Fuzzy DEMATEL Technique ». Dans FMEA Using Uncertainty Theories and MCDM Methods, 133–49. Singapore : Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1466-6_9.

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Gedeon, Tom D., Tao Lin, Patrick M. Wong et Dilip Tamhane. « Integrating Soft Computing Classification Technique For Linguistic Data in a Petroleum Engineering Application ». Dans Fuzzy Logic, 301–10. Heidelberg : Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1806-2_22.

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Suresh, R. M., et S. Arumugam. « Fuzzy Technique Based Recognition of Handwritten Characters ». Dans Fuzzy Logic and Applications, 297–308. Berlin, Heidelberg : Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/10983652_36.

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Waidelich, F., H. Eichfeld et R. Graumann. « Segmentation of Medical Images Using Fuzzy Technique ». Dans Fuzzy Systems in Medicine, 394–409. Heidelberg : Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1859-8_18.

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Neres, Fernando, Regivan Santiago et Benjamín Bedregal. « On a New Contrapositivisation Technique for Fuzzy Implications Constructed from Grouping Functions ». Dans Applications of Fuzzy Techniques, 172–84. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16038-7_18.

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Di Martino, Ferdinando, et Salvatore Sessa. « Fuzzy Transform Technique for Image Autofocus ». Dans Fuzzy Transforms for Image Processing and Data Analysis, 49–60. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44613-0_4.

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Gupta, Shikha, et Muskan. « Fuzzy Logic Control D-STATCOM Technique ». Dans Proceedings of International Conference on Artificial Intelligence and Applications, 173–83. Singapore : Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4992-2_17.

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Ansari, Mohd Zeeshan, M. M. Sufyan Beg et Manoj Kumar. « Enhancement of Fuzzy Rank Aggregation Technique ». Dans Advances in Intelligent Systems and Computing, 127–35. New Delhi : Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2526-3_14.

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Actes de conférences sur le sujet "FUZZY TECHNIQUE"

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Tiwari, Ashutosh, Q. M. Danish Lohani et Pranab K. Muhuri. « Intuitionistic Fuzzy Grey Relational Analysis Sorting Technique ». Dans 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2022. http://dx.doi.org/10.1109/fuzz-ieee55066.2022.9882547.

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Wei, Jyh-Da, Pei-Yu Jiang et Hsu-Fu Hsiao. « A fuzzy inference anti-shake technique for mobile display ». Dans 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2016. http://dx.doi.org/10.1109/fuzz-ieee.2016.7737905.

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Ahmed, Usman, Jerry Chun-Wei Lin, Stefania Tomasiello et Gautam Srivastava. « An Explainable Mental Health Fuzzy Deep Active Learning Technique ». Dans 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2022. http://dx.doi.org/10.1109/fuzz-ieee55066.2022.9882622.

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Kazemian, H. B. « An intelligent video streaming technique in zigbee wireless ». Dans 2009 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2009. http://dx.doi.org/10.1109/fuzzy.2009.5277082.

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Lee, Woo-Joo, Hye-Young Jung, Jin-Hee Yoon et Seung-Hoe Choi. « Forecasting using F-transform based on bootstrap technique ». Dans 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2014. http://dx.doi.org/10.1109/fuzz-ieee.2014.6891756.

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Billiet, Christophe, Antoon Bronselaer et Guy De Tre. « A comparison technique for ill-known time intervals ». Dans 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2016. http://dx.doi.org/10.1109/fuzz-ieee.2016.7737932.

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Abdullayev, P. S. « Fuzzy Statistics Approach for Aviation GTE Condition Estimation Technique ». Dans ASME 8th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2006. http://dx.doi.org/10.1115/esda2006-95791.

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In this paper is shown that the probability-statistic methods application, especially at the early stage of the aviation gas turbine engine (GTE) technical condition diagnosing, when the flight information has property of the fuzzy, limitation and uncertainty is unfounded. Hence is considered the efficiency of application of new technology Soft Computing at these diagnosing stages with the using of the Fuzzy Logic and Neural Networks methods. Training with high accuracy of fuzzy multiple linear and non-linear models (fuzzy regression equations) which received on the statistical fuzzy data basis is made. Thus for GTE technical condition more adequate model making are analysed dynamics of skewness and kurtosis coefficients’ changes. Researches of skewness and kurtosis coefficients values’ changes show that, distributions of GTE work parameters have fuzzy character. Hence consideration of fuzzy skewness and kurtosis coefficients is expedient. Investigation of the basic characteristics changes’ dynamics of GTE work parameters allows to draw conclusion on necessity of the Fuzzy Statistical Analysis at preliminary identification of the engines’ technical condition. Researches of correlation coefficients values’ changes shows also on their fuzzy character. Therefore for models choice the application of the Fuzzy Correlation Analysis results is offered. For checking of models adequacy is considered the Fuzzy Multiple Correlation Coefficient of Fuzzy Multiple Regression. At the information sufficiency is offered to use recurrent algorithm of aviation GTE technical condition identification (Hard Computing technology is used) on measurements of input and output parameters of the multiple linear and nonlinear generalised models at presence of noise measured (the new recursive Least Squares Method (LSM)). The developed GTE condition monitoring system provides stage-by-stage estimation of engine technical conditions. As application of the given technique the estimation of the new operating aviation engine temperature condition was made.
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Biswas, Animesh, et Koushik Bose. « A fuzzy goal programming technique for quadratic multiobjective multilevel programming ». Dans 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2013. http://dx.doi.org/10.1109/fuzz-ieee.2013.6622479.

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Phongsuphap, Sukanya, et Yongyuth Pongsupap. « Evaluation of responsiveness of health systems using fuzzy-based technique ». Dans 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2014. http://dx.doi.org/10.1109/fuzz-ieee.2014.6891574.

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Kajornrit, Jesada, Kok Wai Wong, Chun Che Fung et Yew Soon Ong. « An integrated intelligent technique for monthly rainfall time series prediction ». Dans 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2014. http://dx.doi.org/10.1109/fuzz-ieee.2014.6891619.

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Rapports d'organisations sur le sujet "FUZZY TECHNIQUE"

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Borgwardt, Stefan, et Rafael Peñaloza. Infinitely Valued Gödel Semantics for Expressive Description Logics. Technische Universität Dresden, 2015. http://dx.doi.org/10.25368/2022.217.

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Fuzzy Description Logics (FDLs) combine classical Description Logics with the semantics of Fuzzy Logics in order to represent and reason with vague knowledge. Most FDLs using truth values from the interval [0; 1] have been shown to be undecidable in the presence of a negation constructor and general concept inclusions. One exception are those FDLs whose semantics is based on the infinitely valued Gödel t-norm (G). We extend previous decidability results for the FDL G-ALC to deal with complex role inclusions, nominals, inverse roles, and qualified number restrictions. Our novel approach is based on a combination of the known crispification technique for finitely valued FDLs and an automata-based procedure for reasoning in G-ALC.
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Combs, James E. Advanced Control Techniques with Fuzzy Logic. Fort Belvoir, VA : Defense Technical Information Center, juin 2014. http://dx.doi.org/10.21236/ada604019.

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Buyak, Bogdan B., Ivan M. Tsidylo, Victor I. Repskyi et Vitaliy P. Lyalyuk. Stages of Conceptualization and Formalization in the Design of the Model of the Neuro-Fuzzy Expert System of Professional Selection of Pupils. [б. в.], novembre 2018. http://dx.doi.org/10.31812/123456789/2669.

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The article describes the problem of designing a neuro-fuzzy expert system of professional selection at the stages of conceptualization and formalization, which involves the definition of concepts, relationships and management mechanisms necessary to describe the solution of problems in the chosen subject field. The structural model of the decision making system for determining the professional selection of students for training in IT specialties is substantiated. Three subsystems are proposed as structural components for studying: psychological peculiarities, personal qualities, factual knowledge, abilities and skills of students. The quality of the system’s operation is determined by the use of various techniques for acquiring knowledge on the basis of which the knowledge base of the neuro-fuzzy system and the combination of the use of fuzzy and stochastic data will be formed.
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Borgwardt, Stefan. The Complexity of Fuzzy Description Logics over Finite Lattices with Nominals. Technische Universität Dresden, 2014. http://dx.doi.org/10.25368/2022.204.

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The complexity of reasoning in fuzzy description logics (DLs) over finite lattices usually does not exceed that of the underlying classical DLs. This has recently been shown for the logics between L-IALC and L-ISCHI using a combination of automata- and tableau-based techniques. In this report, this approach is modified to deal with nominals and constants in L-ISCHOI. Reasoning w.r.t. general TBoxes is ExpTime-complete, and PSpace-completeness is shown under the restriction to acyclic terminologies in two sublogics. The latter implies two previously unknown complexity results for the classical DLs ALCHO and SO.
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Borgwardt, Stefan, et Rafael Peñaloza. A Tableau Algorithm for SROIQ under Infinitely Valued Gödel Semantics. Technische Universität Dresden, 2015. http://dx.doi.org/10.25368/2022.223.

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Fuzzy description logics (FDLs) are knowledge representation formalisms capable of dealing with imprecise knowledge by allowing intermediate membership degrees in the interpretation of concepts and roles. One option for dealing with these intermediate degrees is to use the so-called Gödel semantics. Despite its apparent simplicity, developing reasoning techniques for expressive FDLs under this semantics is a hard task. We present a tableau algorithm for deciding consistency of a SROIQ ontology under Gödel semantics. This is the first algorithm that can handle the full expressivity of SROIQ as well as the full Gödel semantics.
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Karam, Sofia, Morteza Nagahi, Vidanelage Dayarathna, Junfeng Ma, Raed Jaradat et Michael Hamilton. Integrating systems thinking skills with multi-criteria decision-making technology to recruit employee candidates. Engineer Research and Development Center (U.S.), juin 2021. http://dx.doi.org/10.21079/11681/41026.

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The emergence of modern complex systems is often exacerbated by a proliferation of information and complication of technologies. Because current complex systems challenges can limit an organization's ability to efficiently handle socio-technical systems, it is essential to provide methods and techniques that count on individuals' systems skills. When selecting future employees, companies must constantly refresh their recruitment methods in order to find capable candidates with the required level of systemic skills who are better fit for their organization's requirements and objectives. The purpose of this study is to use systems thinking skills as a supplemental selection tool when recruiting prospective employees. To the best of our knowledge, there is no prior research that studied the use of systems thinking skills for recruiting purposes. The proposed framework offers an established tool to HRM professionals for assessing and screening of prospective employees of an organization based on their level of systems thinking skills while controlling uncertainties of complex decision-making environment with the fuzzy linguistic approach. This framework works as an expert system to find the most appropriate candidate for the organization to enhance the human capital for the organization.
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Willson. L51756 State of the Art Intelligent Control for Large Engines. Chantilly, Virginia : Pipeline Research Council International, Inc. (PRCI), septembre 1996. http://dx.doi.org/10.55274/r0010423.

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Computers have become a vital part of the control of pipeline compressors and compressor stations. For many tasks, computers have helped to improve accuracy, reliability, and safety, and have reduced operating costs. Computers excel at repetitive, precise tasks that humans perform poorly - calculation, measurement, statistical analysis, control, etc. Computers are used to perform these type of precise tasks at compressor stations: engine / turbine speed control, ignition control, horsepower estimation, or control of complicated sequences of events during startup and/or shutdown. For other tasks, however, computers perform very poorly at tasks that humans find to be trivial. A discussion of the differences in the way humans and computer process information is crucial to an understanding of the field of artificial intelligence. In this project, several artificial intelligence/ intelligent control systems were examined: heuristic search techniques, adaptive control, expert systems, fuzzy logic, neural networks, and genetic algorithms. Of these, neural networks showed the most potential for use on large bore engines because of their ability to recognize patterns in incomplete, noisy data. Two sets of experimental tests were conducted to test the predictive capabilities of neural networks. The first involved predicting the ignition timing from combustion pressure histories; the best networks responded within a specified tolerance level 90% to 98.8% of the time. In the second experiment, neural networks were used to predict NOx, A/F ratio, and fuel consumption. NOx prediction accuracy was 91.4%, A/F ratio accuracy was 82.9%, and fuel consumption accuracy was 52.9%. This report documents the assessment of the state of the art of artificial intelligence for application to the monitoring and control of large-bore natural gas engines.
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Event-Triggered Adaptive Robust Control for Lateral Stability of Steer-by-Wire Vehicles with Abrupt Nonlinear Faults. SAE International, juillet 2022. http://dx.doi.org/10.4271/2022-01-5056.

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Because autonomous vehicles (AVs) equipped with active front steering have the features of time varying, uncertainties, high rate of fault, and high burden on the in-vehicle networks, this article studies the adaptive robust control problem for improving lateral stability in steer-by-wire (SBW) vehicles in the presence of abrupt nonlinear faults. First, an upper-level robust H∞ controller is designed to obtain the desired front-wheel steering angle for driving both the yaw rate and the sideslip angle to reach their correct values. Takagi-Sugeno (T-S) fuzzy modeling method, which has shown the extraordinary ability in coping with the issue of nonlinear, is applied to deal with the challenge of the changing longitudinal velocity. The output of the upper controller can be calculated by a parallel distributed compensation (PDC) scheme. Then an event-triggered adaptive fault-tolerant lower controller (ET-AFTC) is proposed to drive the whole SBW system driving the desired steering angle offered by the upper controller with fewer communication resources and strong robustness. By employing a backstepping technique, the tracking performance is improved. The dynamic surface control (DSC) approach is used to avoid the problem of repeated differentiations, and Nussbaum function is adopted to overcome the difficulty of unknown nonlinear control gain. Both the stability of the upper and lower controllers can be guaranteed by Lyapunov functions. Finally, the simulations of Matlab/Simulink are given to show that the proposed control strategy is effectively able to deal with the abrupt nonlinear fault via less communication resources and perform better in ensuring the yaw stability of the vehicle.
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