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نتیجه جستجو - Fuzzy Entropy

تعداد مقالات یافته شده: 6
ردیف عنوان نوع
1 Risk assessment of the UPIoT construction in China using combined dynamic weighting method under IFGDM environment
ارزیابی ریسک ساخت و ساز UPIoT در چین با استفاده از روش توزین وزن ترکیبی در محیط IFGDM-2020
Large-scale integration of renewable energy systems poses challenges to the ubiquitous power Internet of things (UPIoT) construction in China. This paper aims to study beyond these challenges from a risk assessment per- spective, using the combined dynamic weighting evidence fusion (CDWEF) method under the intuitionistic fuzzy group decision-making (IFGDM) environment. The UPIoT construction risk is identified and characterized by a 17-indicator system which is scored by intuitionistic fuzzy relations (IFRs) from experts. The IFRs are corrected by the dynamic expert weight determined from both the intuitionistic fuzzy entropy and conflicts among IFRs. The IF-AHP-DEMATEL method is adopted to determine the combined indicator weight for correcting the risk mass functions, which are obtained from the IFRs with the evidence fusion theory. The proposed risk assessment method is validated in a case study, indicating that the UPIoT construction risk in China is high in communication networks and business innovation.
Keywords: The ubiquitous power Internet of things (UPIoT) | intuitionistic fuzzy group decision-making | combined dynamic weighting | intuitionistic fuzzy AHP-DEMATEL | risk assessment
مقاله انگلیسی
2 Quantum recurrent encoder–decoder neural network for performance trend prediction of rotating machinery
شبکه عصبی رمزگذار- رمزگذار مکرر کوانتومی برای پیش بینی روند عملکرد ماشین های چرخشی-2020
Traditional neural networks generally neglect the primary and secondary relationships of input information and process the information indiscriminately, which leads to their bad nonlinear approximation capacity and low generalization ability. As a result, traditional neural networks always show poor prediction accuracy in the performance degradation trend prediction of rotating machinery (RM). In view of this, a novel neural network called quantum recurrent encoder–decoder neural network (QREDNN) is proposed in this paper. In QREDNN, the attention mechanism is used to simultaneously reconstruct encoder and decoder of QREDNN, so that QREDNN can fully excavate and pay attention to important information but suppress the interference of redundant information to obtain better nonlinear approximation capacity. On the other hand, the quantum neuron is used to construct a new quantum gated recurrent unit (QGRU) in which activation values and weights are represented by quantum rotation matrices. The QGRU can traverse the solution space more finely and has a lot of multiple attractors, so it can replace the traditional recurrent unit of the encoder and decoder and enhance the generalization ability and response speed of QREDNN. Moreover, the Levenberg– Marquardt (LM) algorithm is introduced to improve the update speeds of the rotation angles of quantum rotation matrices and the attention parameters of QREDNN. Based on the superiorities of QREDNN, a new performance trend prediction method for RM is proposed, in which the denoised fuzzy entropy (DFE) of vibration acceleration signal of RM is input into QREDNN as the performance degradation feature for predicting the performance degradation trend of RM. The examples of predicting the performance trend of rolling bearings demonstrate the effectiveness of our proposed method.
Keywords: Quantum recurrent encoder–decoder | neural network (QREDNN) | Artificial intelligence | Attention mechanism | Quantum neuron | Performance trend prediction | Rotating machinery
مقاله انگلیسی
3 Pythagorean fuzzy VIKOR approaches based on TODIM for evaluating internet banking website quality of Ghanaian banking industry
رویکردهای VIKOR فازی Pythagorean مبتنی بر TODIM برای ارزیابی کیفیت وب سایت بانکی اینترنتی صنعت بانکی غنا-2019
With the rapid development of Internet banking technology in Ghana, the website quality evaluation is the essential core of the customer, which is regarded as a multi-criteria decision making (MCDM) problem. Due to the uncertainty of Internet banking, the evaluation of criteria may be measured by Pythagorean fuzzy numbers (PFNs). In addition, the customer usually does not exhibit complete rationality in the decision procedure. Based on the traditional VIKOR (Vise Kriterijumska Optimizacija I Kompromisno Resenje) method of MCDM, this paper provides a new perspective of a compromised solution, which can handle the decision maker’s psychological behavior by inducing TODIM (a Portuguese acronym meaning Interactive Multi-Criteria Decision Making). By defining Pythagorean fuzzy entropy and cross-entropy measures, we study the determination of the weights of the criteria in advance. Then, considering the psychological behavior of the customer, we design two types of strategies for the combination between TODIM and VIKOR. Meanwhile, the corresponding methods have been developed, i.e., Approaches I and II. After that, a simulated example of ranking Internet banking websites in the Ghanaian banking industry is given to illustrate the validity and applicability of our proposed approaches. Finally, we utilize the Wilcoxon signed-rank test and then discuss the differences among VIKOR, TODIM and our proposed methods.
Keywords: Pythagorean fuzzy sets | VIKOR | TODIM | Multi-criteria decision making | Internet banking
مقاله انگلیسی
4 On Distributed Fuzzy Decision Trees for Big Data
درخت تصمیم گیری فازی توزیع شده برای داده های بزرگ-2018
Fuzzy decision trees (FDTs) have shown to be an effective solution in the framework of fuzzy classification. The approaches proposed so far to FDT learning, however, have generally neglected time and space requirements. In this paper, we propose a distributed FDT learning scheme shaped according to the MapReduceprogrammingmodelforgeneratingbothbinaryandmultiway FDTs from big data. The scheme relies on a novel distributed fuzzy discretizer that generates a strong fuzzy partition for each continuous attribute based on fuzzy information entropy. The fuzzy partitions are, therefore, used as an input to the FDT learning algorithm, which employs fuzzy information gain for selecting the attributes at the decision nodes. We have implemented the FDT learning scheme on the Apache Spark framework. We have used ten real-world publicly available big datasets for evaluating the behavior of the scheme along three dimensions: 1) performance in terms of classification accuracy, model complexity, and execution time; 2) scalability varying the number of computing units; and 3) ability to efficiently accommodate an increasing dataset size. We have demonstrated that the proposed scheme turns out to be suitable for managing big datasets even with a modest commodity hardware support. Finally, we have used the distributed decision tree learning algorithm implemented in the MLLib library and the Chi-FRBCS-BigData algorithm, a MapReduce distributed fuzzy rule-based classification system, for comparative analysis
Index Terms: Apache spark, big data, fuzzy decision trees (FDTs), fuzzy discretizer, fuzzy entropy, fuzzy partitioning,MapReduce
مقاله انگلیسی
5 A Distributed Fuzzy Associative Classifier for Big Data
طبقه بندی کننده انجمنی توزیع شده فازی برای داده های بزرگ-2018
Fuzzy associative classification has not been widely analyzed in the literature, although associative classifiers (ACs) have proved to be very effective in different real domain applications. The main reason is that learning fuzzy ACs is a very heavy task, especially when dealing with large datasets. To overcome this drawback, in this paper, we propose an efficient distributed fuzzy associative classification approach based on the MapReduce paradigm. The approach exploits a novel distributed discretizer based on fuzzy entropy for efficiently generating fuzzy partitions of the attributes. Then, a set of candidate fuzzy association rules is generated by employing a distributed fuzzy extension of the well-known FP-Growth algorithm. Finally, this set is pruned by using three purposely adapted types of pruning. We implemented our approach on the popular Hadoop framework. Hadoop allows distributing storage and processing of very large data sets on computer clusters built from commodity hardware. We have performed an extensive experimentation and a detailed analysis of the results using six very large datasets with up to 11 000 000 instances. We have also experimented different types of reasoning methods. Focusing on accuracy, model complexity, computation time, and scalability, we compare the results achieved by our approach with those obtained by two distributed nonfuzzy ACs recently proposed in the literature. We highlight that, although the accuracies result to be comparable, the complexity, evaluated in terms of number of rules, of the classifiers generated by the fuzzy distributed approach is lower than the one of the nonfuzzy classifiers.
Index Terms: Associative classifier (AC), big data, fuzzy AC (FAC), fuzzy FP-Growth, Hadoop, MapReduce
مقاله انگلیسی
6 On Distributed Fuzzy Decision Trees for Big Data
درخت تصمیم گیری فازی توزیع شده برای داده های بزرگ-2017
Fuzzy decision trees (FDTs) have shown to be an effective solution in the framework of fuzzy classification. The approaches proposed so far to FDT learning, however, have generally neglected time and space requirements. In this paper, we propose a distributed FDT learning scheme shaped according to the MapReduce programming model for generating both binary and multi-way FDTs from big data. The scheme relies on a novel distributed fuzzy discretizer that generates a strong fuzzy partition for each continuous attribute based on fuzzy information entropy. The fuzzy partitions are therefore used as input to the FDT learning algorithm, which employs fuzzy information gain for selecting the attributes at the decision nodes. We have implemented the FDT learning scheme on the Apache Spark framework. We have used ten real-world publicly available big datasets for evaluating the behavior of the scheme along three dimensions: i) performance in terms of classification accuracy, model complexity and execution time, ii) scalability varying the number of computing units and iii) ability to efficiently accommodate an increasing dataset size. We have demonstrated that the proposed scheme turns out to be suitable for managing big datasets even with modest commodity hardware support. Finally, we have used the distributed decision tree learning algorithm implemented in the MLLib library and the Chi-FRBCS-BigData algorithm, a MapReduce distributed fuzzy rule-based classification system, for comparative analysis.
Keywords: Fuzzy Decision Trees | Big Data | Fuzzy Entropy | Fuzzy Discretizer | Apache Spark | MapReduce | Fuzzy Partitioning
مقاله انگلیسی
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بازدید امروز: 1782 :::::::: بازدید دیروز: 0 :::::::: بازدید کل: 1782 :::::::: افراد آنلاین: 46