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Int. J. Fuzzy Syst. Appl.最新文献

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Intelligent Industrial Process Control Based on Fuzzy Logic and Machine Learning 基于模糊逻辑和机器学习的智能工业过程控制
Int. J. Fuzzy Syst. Appl. Pub Date : 2020-01-01 DOI: 10.4018/ijfsa.2020010104
H. Zermane, Rached Kasmi
{"title":"Intelligent Industrial Process Control Based on Fuzzy Logic and Machine Learning","authors":"H. Zermane, Rached Kasmi","doi":"10.4018/ijfsa.2020010104","DOIUrl":"https://doi.org/10.4018/ijfsa.2020010104","url":null,"abstract":"Manufacturing automation is a double-edged sword, on one hand, it increases productivity of production system, cost reduction, reliability, etc. However, on the other hand it increases the complexity of the system. This has led to the need of efficient solutions such as artificial techniques. Data and experiences are extracted from experts that usually rely on common sense when they solve problems. They also use vague and ambiguous terms. However, knowledge engineer would have difficulties providing a computer with the same level of understanding. To resolve this situation, this article proposed fuzzy logic to know how the authors can represent expert knowledge that uses fuzzy terms in supervising complex industrial processes as a first step. As a second step, adopting one of the powerful techniques of machine learning, which is Support Vector Machine (SVM), the authors want to classify data to determine state of the supervision system and learn how to supervise the process preserving habitual linguistic used by operators.","PeriodicalId":233724,"journal":{"name":"Int. J. Fuzzy Syst. Appl.","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125605728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Medical Image Thresholding Using Genetic Algorithm and Fuzzy Membership Functions: A Comparative Study 基于遗传算法和模糊隶属函数的医学图像阈值分割比较研究
Int. J. Fuzzy Syst. Appl. Pub Date : 2019-10-01 DOI: 10.4018/ijfsa.2019100103
Shashwati Mishra, M. Panda
{"title":"Medical Image Thresholding Using Genetic Algorithm and Fuzzy Membership Functions: A Comparative Study","authors":"Shashwati Mishra, M. Panda","doi":"10.4018/ijfsa.2019100103","DOIUrl":"https://doi.org/10.4018/ijfsa.2019100103","url":null,"abstract":"Thresholding is one of the important steps in image analysis process and used extensively in different image processing techniques. Medical image segmentation plays a very important role in surgery planning, identification of tumours, diagnosis of organs, etc. In this article, a novel approach for medical image segmentation is proposed using a hybrid technique of genetic algorithm and fuzzy logic. Fuzzy logic can handle uncertain and imprecise information. Genetic algorithms help in global optimization, gives good results in noisy environments and supports multi-objective optimization. Gaussian, trapezoidal and triangular membership functions are used separately for calculating the entropy and finding the fitness value. CPU time, Root Mean Square Error, sensitivity, specificity, and accuracy are calculated using the three membership functions separately at threshold levels 2, 3, 4, 5, 7 and 9. MRI images are considered for applying the proposed method and the results are analysed. The experimental results obtained prove the effectiveness and efficiency of the proposed method.","PeriodicalId":233724,"journal":{"name":"Int. J. Fuzzy Syst. Appl.","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115243764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Clustering Hybrid Data Using a Neighborhood Rough Set Based Algorithm and Expounding its Application 基于邻域粗糙集的混合数据聚类算法及其应用
Int. J. Fuzzy Syst. Appl. Pub Date : 2019-10-01 DOI: 10.4018/ijfsa.2019100105
Akarsh Goyal, Rahul Chowdhury
{"title":"Clustering Hybrid Data Using a Neighborhood Rough Set Based Algorithm and Expounding its Application","authors":"Akarsh Goyal, Rahul Chowdhury","doi":"10.4018/ijfsa.2019100105","DOIUrl":"https://doi.org/10.4018/ijfsa.2019100105","url":null,"abstract":"In recent times, an enumerable number of clustering algorithms have been developed whose main function is to make sets of objects have almost the same features. But due to the presence of categorical data values, these algorithms face a challenge in their implementation. Also, some algorithms which are able to take care of categorical data are not able to process uncertainty in the values and therefore have stability issues. Thus, handling categorical data along with uncertainty has been made necessary owing to such difficulties. So, in 2007 an MMR algorithm was developed which was based on basic rough set theory. MMeR was proposed in 2009 which surpassed the results of MMR in taking care of categorical data but cannot be used robustly for hybrid data. In this article, the authors generalize the MMeR algorithm with neighborhood relations and make it a neighborhood rough set model which this article calls MMeNR (Min Mean Neighborhood Roughness). It takes care of the heterogeneous data. Also, the authors have extended the MMeNR method to make it suitable for various applications like geospatial data analysis and epidemiology.","PeriodicalId":233724,"journal":{"name":"Int. J. Fuzzy Syst. Appl.","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130919762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Speckle Noise Reduction in SAR Images Using Fuzzy Inference System 基于模糊推理系统的SAR图像散斑降噪
Int. J. Fuzzy Syst. Appl. Pub Date : 2019-10-01 DOI: 10.4018/ijfsa.2019100104
Vijayakumar Singanamalla, Santhi Vaithyanathan
{"title":"Speckle Noise Reduction in SAR Images Using Fuzzy Inference System","authors":"Vijayakumar Singanamalla, Santhi Vaithyanathan","doi":"10.4018/ijfsa.2019100104","DOIUrl":"https://doi.org/10.4018/ijfsa.2019100104","url":null,"abstract":"In recent years, image processing has played a vital role in major research areas. In this article, a new approach using a fuzzy inference system is proposed for speckle reduction in SAR images. In general, SAR images are predominantly used to monitor coastal regions to detect oil spills, ship wake, sea shores and climate changes. In this article, a gamma distribution model is used in a fuzzy inference system to remove speckle noise from SAR images. The performance of the proposed model is tested using fuzzy inference systems, such as mamdani and sugeno. The experimental results proved the efficiency of the proposed system through objective metrics.","PeriodicalId":233724,"journal":{"name":"Int. J. Fuzzy Syst. Appl.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125424304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Kernelised Rough Sets Based Clustering Algorithms Fused With Firefly Algorithm for Image Segmentation 基于核化粗糙集的聚类算法与萤火虫算法相融合的图像分割
Int. J. Fuzzy Syst. Appl. Pub Date : 2019-10-01 DOI: 10.4018/ijfsa.2019100102
Srujan Chinta
{"title":"Kernelised Rough Sets Based Clustering Algorithms Fused With Firefly Algorithm for Image Segmentation","authors":"Srujan Chinta","doi":"10.4018/ijfsa.2019100102","DOIUrl":"https://doi.org/10.4018/ijfsa.2019100102","url":null,"abstract":"Data clustering methods have been used extensively for image segmentation in the past decade. In one of the author's previous works, this paper has established that combining the traditional clustering algorithms with a meta-heuristic like the Firefly Algorithm improves the stability of the output as well as the speed of convergence. It is well known now that the Euclidean distance as a measure of similarity has certain drawbacks and so in this paper we replace it with kernel functions for the study. In fact, the authors combined Rough Fuzzy C-Means (RFCM) and Rough Intuitionistic Fuzzy C-Means (RIFCM) with Firefly algorithm and replaced Euclidean distance with either Gaussian or Hyper-tangent or Radial basis Kernels. This paper terms these algorithms as Gaussian Kernel based rough Fuzzy C-Means with Firefly Algorithm (GKRFCMFA), Hyper-tangent Kernel based rough Fuzzy C-Means with Firefly Algorithm (HKRFCMFA), Gaussian Kernel based rough Intuitionistic Fuzzy C-Means with Firefly Algorithm (GKRIFCMFA) and Hyper-tangent Kernel based rough Intuitionistic Fuzzy C-Means with Firefly Algorithm (HKRIFCMFA), Radial Basis Kernel based rough Fuzzy C-Means with Firefly Algorithm (RBKRFCMFA) and Radial Basis Kernel based rough Intuitionistic Fuzzy C-Means with Firefly Algorithm (RBKRIFCMFA). In order to establish that these algorithms perform better than the corresponding Euclidean distance-based algorithms, this paper uses measures such as DB and Dunn indices. The input data comprises of three different types of images. Also, this experimentation varies over different number of clusters.","PeriodicalId":233724,"journal":{"name":"Int. J. Fuzzy Syst. Appl.","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122888015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Interval-Valued Doubt Fuzzy Ideals in BCK-Algebras bck代数的区间值怀疑模糊理想
Int. J. Fuzzy Syst. Appl. Pub Date : 2019-10-01 DOI: 10.4018/ijfsa.2019100106
Tripti Bej, M. Pal
{"title":"Interval-Valued Doubt Fuzzy Ideals in BCK-Algebras","authors":"Tripti Bej, M. Pal","doi":"10.4018/ijfsa.2019100106","DOIUrl":"https://doi.org/10.4018/ijfsa.2019100106","url":null,"abstract":"Nearly forty years ago, interval-valued fuzzy sets were propounded by Zadeh as the normal ramification of fuzzy sets. This article focuses on the basics of a theory for such an interval-valued fuzzy set becoming interval-valued doubt fuzzy subalgebra and an interval-valued doubt fuzzy ideal of BCK-algebras. Also, the authors discuss fuzzy translation, fuzzy multiplication of an interval-valued doubt fuzzy subalgebra/ideal of a BCK-algebra. Besides this, the authors have attempted to substantiate a few common features relating them. At the same time, some properties of interval-valued doubt fuzzy ideals under homomorphism are investigated and the product of interval-valued doubt fuzzy ideals in BCK-algebras is also established.","PeriodicalId":233724,"journal":{"name":"Int. J. Fuzzy Syst. Appl.","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127951738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Intuitionistic Fuzzy Possibility Degree Measure for Ordering of IVIFNs with Its Application to MCDM IVIFNs排序的直觉模糊可能性度测度及其在MCDM中的应用
Int. J. Fuzzy Syst. Appl. Pub Date : 2019-10-01 DOI: 10.4018/ijfsa.2019100101
A. Biswas, Samir Kumar
{"title":"Intuitionistic Fuzzy Possibility Degree Measure for Ordering of IVIFNs with Its Application to MCDM","authors":"A. Biswas, Samir Kumar","doi":"10.4018/ijfsa.2019100101","DOIUrl":"https://doi.org/10.4018/ijfsa.2019100101","url":null,"abstract":"In this article, the concept of an intuitionistic fuzzy possibility degree (IFPD) for ordering several interval-valued intuitionistic fuzzy numbers (IVIFNs) is introduced. The IFPD ranks IVIFNs by distinguishing the comparable and non-comparable components of the joint intervals of membership and non-membership degrees. The incomparable cases of nested joint intervals can also rank respective IVIFNs through the proposed IFPD approach. An intuitionistic fuzzy possibility preference relation based on the proposed IFPD measure for IVIFNs is defined as a more effective tool for modelling uncertainty than existing intuitionistic preference relations. Further, an approach for solving multicriteria interval-valued intuitionistic fuzzy decision-making problems based on IFPD measure of IVIFNs is advanced also provides a possibility degree as supplementary information to the ranking of alternatives. The validity and effectiveness of the advanced approach are demonstrated through two illustrative examples.","PeriodicalId":233724,"journal":{"name":"Int. J. Fuzzy Syst. Appl.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124203475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Fuzzy Lattice Ordered G-modules 模糊格序g模
Int. J. Fuzzy Syst. Appl. Pub Date : 2019-07-01 DOI: 10.4018/IJFSA.2019070104
Ursala Paul, P. Isaac
{"title":"Fuzzy Lattice Ordered G-modules","authors":"Ursala Paul, P. Isaac","doi":"10.4018/IJFSA.2019070104","DOIUrl":"https://doi.org/10.4018/IJFSA.2019070104","url":null,"abstract":"The study of mathematics emphasizes precision, accuracy, and perfection, but in many of the real-life situations, people face ambiguity, vagueness, imprecision, etc. Fuzzy set theory and rough set theory are two innovative tools in mathematics which are used for decision-making in vague and uncertain information systems. Fuzzy algebra has a significant role in the current era of mathematical research and it deals with the algebraic concepts and models of fuzzy sets. The study of various ordered algebraic structures like lattice ordered groups, Riesz spaces, etc., are of great importance in algebra. The theory of lattice ordered G-modules is very useful in the study of lattice ordered groups and similar algebraic structures. In this article, the theories of fuzzy sets and lattice ordered G-modules are synchronized in a suitable manner to evolve a novel concept in mathematics i.e., fuzzy lattice ordered G-modules which would pave the way for new researchers in fuzzy mathematics to explore much more in this field.","PeriodicalId":233724,"journal":{"name":"Int. J. Fuzzy Syst. Appl.","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128125002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
On Generalized Fuzzy Entropy and Fuzzy Divergence Measure with Applications 广义模糊熵和模糊散度测度及其应用
Int. J. Fuzzy Syst. Appl. Pub Date : 2019-07-01 DOI: 10.4018/IJFSA.2019070102
Surender Singh, Sonam Sharma
{"title":"On Generalized Fuzzy Entropy and Fuzzy Divergence Measure with Applications","authors":"Surender Singh, Sonam Sharma","doi":"10.4018/IJFSA.2019070102","DOIUrl":"https://doi.org/10.4018/IJFSA.2019070102","url":null,"abstract":"Entropy in a fuzzy set measures the amount of ambiguity/imprecision presented in the fuzzy set. In this article, the authors introduce a generalized fuzzy entropy measure and demonstrate its effectiveness in Multiple Attribute Decision Making (MADM) and superiority from the point of view of structured linguistic variables. This article also introduces a generalized fuzzy directed divergence and investigates its properties. Further, this article demonstrates the effectiveness of the proposed generalized directed divergence in pattern recognition.","PeriodicalId":233724,"journal":{"name":"Int. J. Fuzzy Syst. Appl.","volume":"25 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120859529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 13
Intuitionistic Trapezoidal Fuzzy MAGDM Problems with Sumudu Transform in Numerical Methods 基于Sumudu变换的直觉梯形模糊MAGDM问题
Int. J. Fuzzy Syst. Appl. Pub Date : 2019-07-01 DOI: 10.4018/IJFSA.2019070101
P. JohnRobinson, S. Jeeva
{"title":"Intuitionistic Trapezoidal Fuzzy MAGDM Problems with Sumudu Transform in Numerical Methods","authors":"P. JohnRobinson, S. Jeeva","doi":"10.4018/IJFSA.2019070101","DOIUrl":"https://doi.org/10.4018/IJFSA.2019070101","url":null,"abstract":"Multiple attribute group decision making (MAGDM) is an important scientific, social, and economic endeavor. The ability to make consistent and correct choices is the essence of any decision process imbued with uncertainty. In situations where the information or data is in the form of an intuitionistic trapezoidal fuzzy numbers, or to construct the MAGDM problem, an intuitionistic trapezoidal fuzzy weighted averaging (ITzFWA) operator and an intuitionistic trapezoidal fuzzy hybrid aggregation (ITzFHA) operator are used. In this article, the decision maker provides the weights for aggregation in the form of an initial value problem of ordinary differential equations based on the study made on the data given by the decision maker. Decision maker weights are derived through sumudu transformation and various other numerical methods by obtaining the solution of differential equations. A numerical illustration is given to show the effectiveness and feasibility of the proposed approach.","PeriodicalId":233724,"journal":{"name":"Int. J. Fuzzy Syst. Appl.","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126633315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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