Gotowe bibliografie tematyczne / Average of the sum of fuzzy entropies

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Gotowa bibliografia na temat „Average of the sum of fuzzy entropies”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 7 lutego 2022

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Artykuły w czasopismach na temat "Average of the sum of fuzzy entropies"

Yan, Xiaoan, Yadong Xu i Minping Jia. "Intelligent Fault Diagnosis of Rolling-Element Bearings Using a Self-Adaptive Hierarchical Multiscale Fuzzy Entropy". Entropy 23, nr 9 (30.08.2021): 1128. http://dx.doi.org/10.3390/e23091128.

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The fuzzy-entropy-based complexity metric approach has achieved fruitful results in bearing fault diagnosis. However, traditional hierarchical fuzzy entropy (HFE) and multiscale fuzzy entropy (MFE) only excavate bearing fault information on different levels or scales, but do not consider bearing fault information on both multiple layers and multiple scales at the same time, thus easily resulting in incomplete fault information extraction and low-rise identification accuracy. Besides, the key parameters of most existing entropy-based complexity metric methods are selected based on specialist experience, which indicates that they lack self-adaptation. To address these problems, this paper proposes a new intelligent bearing fault diagnosis method based on self-adaptive hierarchical multiscale fuzzy entropy. On the one hand, by integrating the merits of HFE and MFE, a novel complexity metric method, named hierarchical multiscale fuzzy entropy (HMFE), is presented to extract a multidimensional feature matrix of the original bearing vibration signal, where the important parameters of HMFE are automatically determined by using the bird swarm algorithm (BSA). On the other hand, a nonlinear feature matrix classifier with strong robustness, known as support matrix machine (SMM), is introduced for learning the discriminant fault information directly from the extracted multidimensional feature matrix and automatically identifying different bearing health conditions. Two experimental results on bearing fault diagnosis show that the proposed method can obtain average identification accuracies of 99.92% and 99.83%, respectively, which are higher those of several representative entropies reported by this paper. Moreover, in the two experiments, the standard deviations of identification accuracy of the proposed method were, respectively, 0.1687 and 0.2705, which are also greater than those of the comparison methods mentioned in this paper. The effectiveness and superiority of the proposed method are verified by the experimental results.

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Pavlačka, Ondřej. "Note on the lack of equality between fuzzy weighted average and fuzzy convex sum". Fuzzy Sets and Systems 213 (luty 2013): 102–5. http://dx.doi.org/10.1016/j.fss.2012.08.003.

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Wei, Guiwu, i Mao Lu. "Pythagorean Hesitant Fuzzy Hamacher Aggregation Operators in Multiple-Attribute Decision Making". Journal of Intelligent Systems 28, nr 5 (17.10.2017): 759–76. http://dx.doi.org/10.1515/jisys-2017-0106.

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Abstract The Hamacher product is a t-norm and the Hamacher sum is a t-conorm. They are good alternatives to the algebraic product and the algebraic sum, respectively. Nevertheless, it seems that most of the existing hesitant fuzzy aggregation operators are based on algebraic operations. In this paper, we utilize Hamacher operations to develop some Pythagorean hesitant fuzzy aggregation operators: Pythagorean hesitant fuzzy Hamacher weighted average operator, Pythagorean hesitant fuzzy Hamacher weighted geometric operator, Pythagorean hesitant fuzzy Hamacher ordered weighted average operator, Pythagorean hesitant fuzzy Hamacher ordered weighted geometric operator, Pythagorean hesitant fuzzy Hamacher hybrid average operator, and Pythagorean hesitant fuzzy Hamacher hybrid geometric operator. The prominent characteristics of these proposed operators are studied. Then, we utilize these operators to develop some approaches for solving the Pythagorean hesitant fuzzy multiple-attribute decision-making problems. Finally, a practical example is given to verify the developed approach and to demonstrate its practicality and effectiveness.

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Cui, Dong, Jinhuan Wang, Zhijie Bian, Qiuli Li, Lei Wang i Xiaoli Li. "Analysis of entropies based on empirical mode decomposition in amnesic mild cognitive impairment of diabetes mellitus". Journal of Innovative Optical Health Sciences 08, nr 05 (21.08.2015): 1550010. http://dx.doi.org/10.1142/s1793545815500108.

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EEG characteristics that correlate with the cognitive functions are important in detecting mild cognitive impairment (MCI) in T2DM. To investigate the complexity between aMCI group and age-matched non-aMCI control group in T2DM, six entropies combining empirical mode decomposition (EMD), including Approximate entropy (ApEn), Sample entropy (SaEn), Fuzzy entropy (FEn), Permutation entropy (PEn), Power spectrum entropy (PsEn) and Wavelet entropy (WEn) were used in the study. A feature extraction technique based on maximization of the area under the curve (AUC) and a support vector machine (SVM) were subsequently used to for features selection and classification. Finally, Pearson's linear correlation was employed to study associations between these entropies and cognitive functions. Compared to other entropies, FEn had a higher classification accuracy, sensitivity and specificity of 68%, 67.1% and 71.9%, respectively. Top 43 salient features achieved classification accuracy, sensitivity and specificity of 73.8%, 72.3% and 77.9%, respectively. P4, T4 and C4 were the highest ranking salient electrodes. Correlation analysis showed that FEn based on EMD was positively correlated to memory at electrodes F7, F8 and P4, and PsEn based on EMD was positively correlated to Montreal cognitive assessment (MoCA) and memory at electrode T4. In sum, FEn based on EMD in right-temporal and occipital regions may be more suitable for early diagnosis of the MCI with T2DM.

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Li, Yanping, Qi Wang, Tao Wang, Jian Pei i Shuo Zhang. "Feature Extraction of EEG Signals Based on Local Mean Decomposition and Fuzzy Entropy". International Journal of Pattern Recognition and Artificial Intelligence 34, nr 12 (13.09.2020): 2058017. http://dx.doi.org/10.1142/s0218001420580173.

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An improved feature extraction method is proposed aiming at the recognition of motor imagined electroencephalogram (EEG) signals. Using local mean decomposition, the algorithm decomposes the original signal into a series of product function (PF) components, and meaningless PF components are removed from EEG signals in the range of mu rhythm and beta rhythm. According to the principle of feature time selection, 4[Formula: see text]s to 6[Formula: see text]s motor imagery EEG signals are selected as classification data, and the sum of fuzzy entropies of second-and third-order PF components of [Formula: see text], [Formula: see text] lead signals is calculated, respectively. Mean value of fuzzy entropy [Formula: see text] is used as input element to construct EEG feature vector, and support vector machine (SVM) is used to classify and predict EEG signals for recognition. The test results show that this feature extraction method has higher classification accuracy than the empirical mode decomposition method and the total empirical mode decomposition method.

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Zhu, Yahui, i Li Gao. "Decision Method of Probabilistic Hesitant Fuzzy Information Based on Hamacher Aggregation Operators and MULTIMOORA". Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 38, nr 6 (grudzień 2020): 1361–69. http://dx.doi.org/10.1051/jnwpu/20203861361.

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Aiming at solving the problem of probability hesitation fuzzy multi-attribute decision making, a new decision-making method of probability hesitation fuzzy multi-attribute is proposed in this paper, based on Hamacher operations and MULTIMOORA method. Firstly, probability hesitation fuzzy Hamacher operations are defined, including sum, product, scalar multiplication and exponentiation, and their properties are studied. On this basis, probability hesitation fuzzy Hamacher weighted average operator and probability hesitation fuzzy Hamacher weighted geometric average operator are proposed, and their properties are also studied. Secondly, alternative from multiple perspectives are chosen and compared by using the MULTIMOORA method. Finally, the effectiveness and feasibility of the decision-making method are verified by an example.

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Ganesh Kumar, Vinod Jangid, Gaurav Sharma,. "Strategic Management in Marketing: A Game Theoretic Approach". Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, nr 6 (5.04.2021): 2518–24. http://dx.doi.org/10.17762/turcomat.v12i6.5697.

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In marketing, a real-world dilemma emerging between two rivals, McDonald's and Burger King, is investigated. Both firms use three strategies: discounted pricing, status quo, and aggressive commercial. In such cases, ambiguity is a determining factor. To deal with confusion in payoffs, octagonal fuzzy numbers are used. To rank fuzzy numbers, the average of odd positions, average of even positions, and quartile deviations are used. To solve the reduced modelled two competitors zero sum fuzzy matrix games, the proposed ranking methods are used. Finally, the findings are compared to current approaches that are quite similar to the proposed approach.

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Madarro-Capó, Evaristo José, Carlos Miguel Legón-Pérez, Omar Rojas i Guillermo Sosa-Gómez. "Information Theory Based Evaluation of the RC4 Stream Cipher Outputs". Entropy 23, nr 7 (14.07.2021): 896. http://dx.doi.org/10.3390/e23070896.

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This paper presents a criterion, based on information theory, to measure the amount of average information provided by the sequences of outputs of the RC4 on the internal state. The test statistic used is the sum of the maximum plausible estimates of the entropies H(jt|zt), corresponding to the probability distributions P(jt|zt) of the sequences of random variables (jt)t∈T and (zt)t∈T, independent, but not identically distributed, where zt are the known values of the outputs, while jt is one of the unknown elements of the internal state of the RC4. It is experimentally demonstrated that the test statistic allows for determining the most vulnerable RC4 outputs, and it is proposed to be used as a vulnerability metric for each RC4 output sequence concerning the iterative probabilistic attack.

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Wang, Ping, Guiwu Wei, Jie Wang, Rui Lin i Yu Wei. "Dual Hesitant q-Rung Orthopair Fuzzy Hamacher Aggregation Operators and their Applications in Scheme Selection of Construction Project". Symmetry 11, nr 6 (6.06.2019): 771. http://dx.doi.org/10.3390/sym11060771.

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The q-rung orthopair fuzzy set (q-ROFS), which is the extension of intuitionistic fuzzy set (IFS) and Pythagorean fuzzy set (PFS), satisfies the sum of q-th power of membership degree and nonmembership degree is limited 1. Evidently, the q-ROFS can depict more fuzzy assessment information and consider decision-maker’s (DM’s) hesitance. Thus, the concept of a dual hesitant q-rung orthopair fuzzy set (DHq-ROFS) is developed in this paper. Then, based on Hamacher operation laws, weighting average (WA) operator and weighting geometric (WG) operator, some dual hesitant q-rung orthopair fuzzy Hamacher aggregation operators are developed, such as the dual hesitant q-rung orthopair fuzzy Hamacher weighting average (DHq-ROFHWA) operator, the dual hesitant q-rung orthopair fuzzy Hamacher weighting geometric (DHq-ROFHWG) operator, the dual hesitant q-rung orthopair fuzzy Hamacher ordered weighted average (DHq-ROFHOWA) operator, the dual hesitant q-rung orthopair fuzzy Hamacher ordered weighting geometric (DHq-ROFHOWG) operator, the dual hesitant q-rung orthopair fuzzy Hamacher hybrid average (DHq-ROFHHA) operator, and the dual hesitant q-rung orthopair fuzzy Hamacher hybrid geometric (DHq-ROFHHG) operator. The precious merits and some particular cases of above mentioned aggregation operators are briefly introduced. In the end, an actual application for scheme selection of construction project is provided to testify the proposed operators and deliver a comparative analysis.

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Savarimuthu, Charles, i Arockiam L. "Pairwise Fuzzy Ordered Weighted Average Algorithm-Gaussian Mixture Model for Feature Reduction". INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 6, nr 1 (30.05.2013): 287–301. http://dx.doi.org/10.24297/ijct.v6i1.4457.

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Feature Reduction is a kind of dimensionality reduction of feature space. There are a number of approaches are used to identify the significant features but they are not using the weighing approach. The weighing approach is quite useful for obtaining the significant features and removing the insignificant and irrelevant features using OWA formulation. The aim of this approach is to obtain the significant features and removing insignificant features by using the pairwise approach. This approach is helpful to find the weights of pairwise features at the same time, which leads to remove the insignificant features from the feature space using OWA. The significance of the OWA formulation is that, the paired features are identified in priori and their sum of weights are equal to 1. OWA criterion is introduced to obtain the significant features that are useful for predicting the accuracy of the cluster in GMM.

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Streszczenia konferencji na temat "Average of the sum of fuzzy entropies"

Tufano, Dante A., i Zahra Sotoudeh. "Introducing Entropy for the Statistical Energy Analysis of an Artificially Damped Oscillator". W ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-50591.

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The purpose of this paper is to introduce the concept of entropy for a main resonator attached to a “fuzzy structure”. This structure is described explicitly using the Lagrangian method, and is treated as a layer of discrete resonators. A generic entropy formulation is then developed for the layer of resonators, which is used to determine the individual oscillator entropies. The combined entropy of the linear resonator system is then determined and compared numerically to the sum of the individual oscillator entropies. The entropy behavior of the system is then related to the energy behavior of the system and explained in regards to the the “artificial damping” of the main resonator.

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