An Improved SVM-Based Cognitive Diagnosis Algorithm for Operation States of Distribution Grid

Yang, Jun; Gong, Liyu; Tang, Youxi; Yan, Jun; He, Haibo; Zhang, Leiqi; Li, Gang

doi:10.1007/s12559-015-9323-2

Cited by 15 publications

(13 citation statements)

References 22 publications

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“…We suppose that µ(c 1 ) = 0.3, µ(c 2 ) = 0.25 and µ(c 3 ) = 0.37. Then α = 0.2795 is determined by using Equation (5). According to the Equation (4) fuzzy measures of criteria sets of C = {c 1 , c 2 , c 3 }, we can obtain µ(c 1 , c 2 ) = 0.5710, µ(c 1 , c 3 ) = 0.7010, µ(c 2 , c 3 ) = 0.6459, and µ(c 1 , c 2 , c 3 ) = 1.…”

mentioning

confidence: 99%

Prioritized Aggregation Operators and Correlated Aggregation Operators for Hesitant 2-Tuple Linguistic Variables

Wang

Liu

2018

Symmetry

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Abstract:The aggregation operator is a potential tool to fuse the information derived from multisources, which has been applied in group decision, combination classification and scheduling clusters successfully. To better characterize complex decision situations and capture complex opinions of decision-makers (DMs), aggregation operators are required to be explored from different viewpoints. In view of information fusion of hesitant 2-tuple linguistic variables, this paper establishes four new aggregation operators, which are called the hesitant 2-tuple linguistic prioritized weighted averaging (H2TLPWA) aggregation operator, hesitant 2-tuple linguistic prioritized weighted geometric (H2TLPWG) aggregation operator, hesitant 2-tuple linguistic correlated averaging (H2TLCA) aggregation operator, and hesitant 2-tuple linguistic correlated geometric (H2TLCG) aggregation operator, respectively. The H2TLPWA aggregation operator and H2TLPWG aggregation operator can characterize the prioritization relationship of the aggregated arguments. The H2TLCA aggregation operator and H2TLCG aggregation operator can describe dependencies between criteria in decision-making problem solving. Moreover all aggregation operation operators have the properties of idempotency, boundedness and monotonicity, and the H2TLCA aggregation operator and H2TLCG aggregation operator are also verified to be symmetric functions. In addition, the H2TLPWA aggregation operator and H2TLCA aggregation operator are employed to settle multicriteria decision-making problems with hesitant 2-tuple linguistic terms. By virtue of predefining discrete initial linguistic labels with symmetrical distribution, the detailed steps of the decision-making process with an example are given to illustrate their practicality and effectiveness.

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confidence: 99%

Prioritized Aggregation Operators and Correlated Aggregation Operators for Hesitant 2-Tuple Linguistic Variables

Wang

Liu

2018

Symmetry

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“…The charge of the batteries and control of the system were carried out by a charge controller. This controller, which is fed by the solar and the wind power, can feed the loads while charging [15,16]. General connection diagram of the entire system is given in Fig.…”

Section: Connection Of Battery and Loadsmentioning

confidence: 99%

The Design Steps of a Hybrid Energy System

Şahin

Okumuş

2017

Acta Phys. Pol. A

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In this paper the design stages of a mixed photovoltaic-wind energy system, supplied with solar and wind energy was discussed. First, general information about the employed wind turbines and solar panels is given and their basic characteristics are addressed. Then, essential information for the system setup and measurements was given. The system setup, operation of the system, the ways to store energy and how energy is consumed were described and the results were interpreted. This study serves as a background for the experimental studies in the future.

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“…Due to these capabilities, the SVM has been applied on various applications and shown efficient performance in a wide range of challenging real-world problems, such as bio-medical applications [38,3], pattern recognition [55], anomaly detection [29,36], and many others [49,54,56,2,31,35,32].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Feature Selection and Support Vector Machine Optimization Using the Grasshopper Optimization Algorithm

et al. 2018

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Background/ introduction: Support Vector Machine (SVM) is considered to be one of the most powerful learning algorithms and is used for a wide range of real world applications. The efficiency of SVM algorithm and its performance mainly depends on the kernel type and its parameters. Furthermore, the feature subset selection that is used to train the SVM model is another important factor that has a major influence on it classification accuracy. The feature subset selection is a very important step in machine learning, specially when dealing with high dimensional data sets. Most of the previous researches handled these important factors separately. Methods: In this paper, we propose a hybrid approach based on the Grasshopper Optimisation Algorithm (GOA), which is a recent algorithm inspired by the biological behaviour shown in swarms of grasshoppers. The goal of the proposed approach is to optimise the parameters of the SVM model, and locate the best features subset simultaneously. Re-() I. Aljarah • A

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An Improved SVM-Based Cognitive Diagnosis Algorithm for Operation States of Distribution Grid

Cited by 15 publications

References 22 publications

Prioritized Aggregation Operators and Correlated Aggregation Operators for Hesitant 2-Tuple Linguistic Variables

Prioritized Aggregation Operators and Correlated Aggregation Operators for Hesitant 2-Tuple Linguistic Variables

The Design Steps of a Hybrid Energy System

Simultaneous Feature Selection and Support Vector Machine Optimization Using the Grasshopper Optimization Algorithm

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