Статті в журналах: "WEIGHTING METHOD"

1

Zhongguo, Zheng. "Random weighting method forT statistic." Acta Mathematica Sinica 5, no. 1 (March 1989): 87–94. http://dx.doi.org/10.1007/bf02107626.

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2

Zhang, Dixin. "PP multivariate random weighting method." Acta Mathematica Sinica 11, no. 3 (September 1995): 256–66. http://dx.doi.org/10.1007/bf02265391.

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3

Ali, Ghassan Khazal, and Alexander Zamyatin. "A Supervised Term Relevance Weighting Method for Arabic Text Classification." Journal of Advanced Research in Dynamical and Control Systems 11, no. 11 (November 20, 2019): 206–12. http://dx.doi.org/10.5373/jardcs/v11i11/20193189.

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4

Lenzuni, Paolo. "On the Low Frequency Noise Assessment Method." Journal of Low Frequency Noise, Vibration and Active Control 22, no. 2 (June 2003): 53–61. http://dx.doi.org/10.1260/026309203322770301.

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A new assessment method for low frequency noise annoyance is proposed. A new frequency weighting is introduced, and its robustness tested. Possible effects of impulsiveness and tonality are discussed. The method proposed explicitly includes correction factors for the former, while the latter is shown to be insignificant in this context. The method is cross-checked with existing methods based on frequency weightings, and its performance is objectively quantified using synthetic indicators.

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5

Cebi, Selcuk, and Cengiz Kahraman. "A new weighted fuzzy information axiom method in production research." Journal of Enterprise Information Management 32, no. 1 (February 11, 2019): 170–90. http://dx.doi.org/10.1108/jeim-03-2017-0046.

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Purpose The purpose of this paper is to propose a novel weighting algorithm for fuzzy information axiom (IA) and to apply it to the evaluation process of 3D printers. Design/methodology/approach As a decision-making tool, IA method is presented to evaluate the performance of any design. Then, weighted IA methods are investigated and a new weighting procedure is introduced to the literature. Then, the existing axiomatic design methods and the proposed new method are classified into two groups: weighting based on information content and weighting based on design ranges. The weighting based on information content approach consists of four methods including pessimistic and optimistic approaches. The philosophy of the weighting based on design ranges is to narrow design ranges in order to decrease fuzziness in the model. To prove the robustness and the performance of the proposed weighting method, the results are compared with the existing methods in the literature. Then, the new approach is applied to evaluate 3D printers. Findings The results of the proposed study show that the proposed weighting algorithm has better performance than the old ones for IA. Therefore, the proposed weighting algorithm should be used for the weighting tool of IA thereafter. Originality/value An effective weighting method compatible with the philosophy of IA method has been proposed. Furthermore, the performances of 3D printers are compared by using the proposed method.

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6

Zhao, Fuzhang. "Weighting Factors for Single-Step Trapezoidal Method." Journal of Heat Transfer 128, no. 4 (November 7, 2005): 409–12. http://dx.doi.org/10.1115/1.2175155.

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The uses of a weighting factor along with a time step in a single-step trapezoidal method to solve a first-order parabolic system have been systematically studied. The weighting factors are used in two main types: constants and variables. The most commonly used constant weighting factors can be defined by the ratio of the Fibonacci sequence. Among them, the optimal weighting factor is 0.618, resulting in a balance between the overall accuracy and efficiency. With the finite element formulation, the space and time dimensions can be discretized separately. For the time discretization only, there exists a zero-error dimensionless time step if a weighting factor is within the range of 0.5–1.0. By taking advantage of the zero-error condition, the weighting factor can be correlated with a time step. The influence of spatial dimensions is lumped into a nonzero eigenvalue of the system. Through validity tests of two benchmark linear problems, the variable weighting factor for a single-step trapezoidal method is shown to be accurate, efficient, and stable. The relevant features have been captured.

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7

Zhao, Wenbin, Changlai Xiao, Yunxu Chai, Xiaoya Feng, Xiujuan Liang, and Zhang Fang. "Application of a New Improved Weighting Method, ESO Method Combined with Fuzzy Synthetic Method, in Water Quality Evaluation of Chagan Lake." Water 13, no. 10 (May 20, 2021): 1424. http://dx.doi.org/10.3390/w13101424.

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The existing weighting methods mainly comprise subjective and objective weighting and have a certain degree of subjectivity, with certain requirements for the professional ability of the users and unstable results. Therefore, an improved weighting method based on the entropy weight, over-standard multiple, and single-factor evaluation methods, referred to as the ESO method, is proposed. The advantages and advancements of the ESO method are demonstrated in this study by combining it with the fuzzy synthetic evaluation method to evaluate the water quality of Chagan Lake wetland from 2007 to 2016. The main conclusions of this study are as follows: 1. The ESO method has more comprehensive consideration factors, lower requirements for the professional ability of users, and more stable weighting results than the traditional weighting method. Therefore, it is highly suitable for beginners and frontline staff who are not professionally qualified and cannot accurately conduct subjective weighting. Meanwhile, owing to the amendment rule and emphasis on the local weight of the sample in the ESO method, it is applicable to time-series samples. 2. The ESO method better allocates the amendment weights to indicators with a higher degree of pollution; thus, the final comprehensive evaluation results are relatively conservative. However, in contrast to the single-factor evaluation, the conservatism of ESO method is the result of the comprehensive effect of all samples; thus, the conservative result of the ESO method is more reasonable. 3. The water quality of Chagan Lake in 2009 and 2015 was class IV, which did not meet the standard, while that in remaining the eight years was class III, which met the requirements of the national 13th Five-Year Plan. The results of this study can provide a new approach to weighting calculation methods and a basis for the protection and treatment of the ecological environment of the Chagan Lake wetland.

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8

KAWABE, Taketoshi, Toshio MATSUMURA, Kimio KANAI, and Toshiyuki KITAMORI. "A Frequency Weighting Parameter Identification Method." Transactions of the Society of Instrument and Control Engineers 31, no. 6 (1995): 773–79. http://dx.doi.org/10.9746/sicetr1965.31.773.

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9

Huang, Yi-Fung, and Kun-Li Wen. "Weighting analysis by using grey method." Journal of Discrete Mathematical Sciences and Cryptography 11, no. 1 (February 2008): 33–58. http://dx.doi.org/10.1080/09720529.2008.10698165.

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10

Willems, F. M. J. "The context-tree weighting method: extensions." IEEE Transactions on Information Theory 44, no. 2 (March 1998): 792–98. http://dx.doi.org/10.1109/18.661523.

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11

Zhang, Feng, Zhen Hua Xie, Jiang Tao Cheng, Gao Lun Cui, and Lin Li. "Combination Weighting Method Based on Generalized Mahalanobis Distance and Weighting Relative Entropy." Advanced Materials Research 998-999 (July 2014): 1674–77. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.1674.

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Aimed at combination weighting in multiple attribute decision making, a new approach for combining different weighting vectors is proposed. The proposed approach considers the randomicity of weights themselves and the consistency among weighting vectors, constructs a constrained weighted relative entropy model. Aimed at the disadvantage in the TOPSIS based on Euclidean distance, the TOPSIS based on Mahalanobis distance is adopted to solve the coefficients of optimal weight vector. Finally, an example is conducted and the results show the proposed approach is effective and is more reasonable than three other combination approaches.

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12

Huang, X., and G. F. West. "Effects of weighting functions on Maslov uniform seismograms: A robust weighting method." Bulletin of the Seismological Society of America 87, no. 1 (February 1, 1997): 164–73. http://dx.doi.org/10.1785/bssa0870010164.

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Abstract In principle, Maslov theory provides a uniform asymptotic solution for modeling wave fields in a generally inhomogeneous medium by blending ray theory and Maslov transform theory. However, in a real calculation, it is still not clear how choice of the weighting functions that select the ray and Maslov transform solutions will affect the accuracy of Maslov seismograms. To answer this question, we have investigated two weighting functions: One is the hyperbolic function suggested by Brown (1994), and the other is a trigonometric function we propose. The former relates the weights to the local slope of the Lagrangian manifold cross section, reflecting the opportunity for the ray solution or the Maslov solution to contribute to the wave-field construction. But the transition level of the hyperbolic function is controlled by an artificial coefficient. We found that the hyperbolic function performs accurately, particularly when a wave front is only smoothly bent, but there exists a tolerance range for the value of the coefficient. Outside this range, errors of caustic type or pseudocaustic type arise. Because the range is limited, and because it changes for different modelings, use of the hyperbolic function can become troublesome in intensive modelings where it is impractical to have human supervision. The trigonometric function that we propose works differently. Caustic and pseudocaustic points are located by a root-finding algorithm, and trigonometric weighting functions control switching of the two solutions in a symmetric way between pairs of caustics and pseudocaustics. In most cases, it will provide an accurate result automatically, and it should be considered first for intensive modelings such as those needed in controlled source seismology.

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13

Xie, Yu, Bao Hong Han, and Yun Long Duan. "Study on a Method of Accurate Weighting." Applied Mechanics and Materials 389 (August 2013): 754–57. http://dx.doi.org/10.4028/www.scientific.net/amm.389.754.

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An accurate and automatic weighting method was used to satisfy the command. The system consisted a hopper, a vibration propellant delivering equipment, a particle separation equipment and control system. The main controller and the programmed logic controller were used to control the system to make sure the high precision of particles weighting as host computer and slave computer respectively. The result shows that the method can solve the key technology of the automatic weighting of the small-bore experimental cartridge and offers significance for the automatic weighting of standard particles.

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14

Yang, Kun, Guang Ming Yang, Yong Huang, and Pan De Jing. "Hydraulic Mental Structure Health Diagnosis Weighting Method." Applied Mechanics and Materials 494-495 (February 2014): 925–31. http://dx.doi.org/10.4028/www.scientific.net/amm.494-495.925.

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By means of inductive analysis the health diagnosis weighting methods in engineering, this paper discusses the basic principle, advantages, disadvantages and applicable scope of weighting methods. On the basis, combining with multi-level, multi-standard and multi-factor characteristic of hydraulic metal structures health diagnosis, the methods of AHP, information gain, information diffusion and improved entropy are studied, which are applicable to hydraulic metal structures health diagnosis weighting. Based on fuzzy theory, the fuzzy multi-level comprehensive weighting method is put forward and studied, which combined both subjective and objective method advantages and its complementary. In addition, it provides the necessary theory foundation and new ideas for the development of hydraulic metal structure health diagnosis technology.

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15

Li, Xian Mao, Gao Ming Huang, and Dong Xia. "Spacial Filter of Weighting Method Based on Spectrum Analyse." Advanced Materials Research 605-607 (December 2012): 1890–96. http://dx.doi.org/10.4028/www.scientific.net/amr.605-607.1890.

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The selection of a certain scope angle signal in the traditional method is to switch the hard switches in antennas, this paper proposes a method, which based on a weighting method to filter the signal in certain directions, namely spacial filter. With array antennas，the compositive signal can be acquired, by which the phrase and plus (weighting) of each unit antenna’s signal be adjusted and then the signals be added. In different time, signals can be selected in any scope of directions through adjusting each channels by different weighting. The weighting parameters can be obtained through the analysis of spacial signal and spacial spectrum, and then obtains an appropriate weighting window function. Simulation shows that Hamming window’s weighting is the best among the three representative windows functions. It can obtain a low sidelobe (-44dB) and less rising edge and declining edges. And the paper also give a hardware structure.

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16

张, 丽蓉. "Selection of TOPSIS Method Weights: Comparison of Several Weighting Methods." Statistics and Application 12, no. 04 (2023): 901–9. http://dx.doi.org/10.12677/sa.2023.124095.

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17

Shi, Zhenquan, and Shiping Chen. "A New Knowledge Characteristics Weighting Method Based on Rough Set and Knowledge Granulation." Computational Intelligence and Neuroscience 2018 (May 31, 2018): 1–9. http://dx.doi.org/10.1155/2018/1838639.

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The knowledge characteristics weighting plays an extremely important role in effectively and accurately classifying knowledge. Most of the existing characteristics weighting methods always rely heavily on the experts’ a priori knowledge, while rough set weighting method does not rely on experts’ a priori knowledge and can meet the need of objectivity. However, the current rough set weighting methods could not obtain a balanced redundant characteristic set. Too much redundancy might cause inaccuracy, and less redundancy might cause ineffectiveness. In this paper, a new method based on rough set and knowledge granulation theories is proposed to ascertain the characteristics weight. Experimental results on several UCI data sets demonstrate that the weighting method can effectively avoid subjective arbitrariness and avoid taking the nonredundant characteristics as redundant characteristics.

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18

Goodridge, Wayne S. "Sensitivity Analysis Using Simple Additive Weighting Method." International Journal of Intelligent Systems and Applications 8, no. 5 (August 5, 2016): 27–33. http://dx.doi.org/10.5815/ijisa.2016.05.04.

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19

Golubev, Yu, and D. Ostrovski. "Concentration inequalities for the exponential weighting method." Mathematical Methods of Statistics 23, no. 1 (January 2014): 20–37. http://dx.doi.org/10.3103/s1066530714010025.

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20

Koh, Jin-Hwan, Xiaowen Xu, Beong-Ju Ryu, Jae-Hun Lee, and Jung-Sup Lee. "Data De-weighting in Matrix Pencil Method." Journal of Korea Information and Communications Society 36, no. 8A (August 31, 2011): 741–47. http://dx.doi.org/10.7840/kics.2011.36a.8.741.

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21

Wang, Shuhui, Mingyang Lu, Jidan Mei, and Wenting Cui. "Deconvolved Beamforming Using the Chebyshev Weighting Method." Journal of Marine Science and Application 21, no. 3 (September 2022): 228–35. http://dx.doi.org/10.1007/s11804-022-00286-7.

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22

Willems, F. M. J., Y. M. Shtarkov, and T. J. Tjalkens. "The context-tree weighting method: basic properties." IEEE Transactions on Information Theory 41, no. 3 (May 1995): 653–64. http://dx.doi.org/10.1109/18.382012.

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23

Mert, Ahmet, Niyazi Kılıç, and Erdem Bilgili. "Random subspace method with class separability weighting." Expert Systems 33, no. 3 (March 24, 2016): 275–85. http://dx.doi.org/10.1111/exsy.12149.

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24

Kuzmenko, A. V. "Weighting method of the Fourier-kinoform synthesis." Semiconductor physics, quantum electronics and optoelectronics 11, no. 3 (September 15, 2008): 303–6. http://dx.doi.org/10.15407/spqeo11.03.303.

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25

王, 庭芳. "A Weighting Attribute Method for Classification Problems." Advances in Applied Mathematics 09, no. 05 (2020): 752–58. http://dx.doi.org/10.12677/aam.2020.95089.

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26

Zhou, Tianyue. "Weighting Method for a Linear Mixed Model." Communications in Statistics - Theory and Methods 39, no. 2 (December 11, 2009): 214–27. http://dx.doi.org/10.1080/03610920802677216.

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27

Morris, John D. "Selecting a Predictor Weighting Method by Press." Educational and Psychological Measurement 46, no. 4 (December 1986): 853–69. http://dx.doi.org/10.1177/001316448604600406.

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28

Bonnet, Laurent, and Jean-Claude Rayez. "Gaussian weighting in the quasiclassical trajectory method." Chemical Physics Letters 397, no. 1-3 (October 2004): 106–9. http://dx.doi.org/10.1016/j.cplett.2004.08.068.

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29

Forrest, William F. "Weighting Improves the ‘New Haseman-Elston’ Method." Human Heredity 52, no. 1 (2001): 47–54. http://dx.doi.org/10.1159/000053353.

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30

Gao, Shesheng, Yongmin Zhong, and Wei Li. "Random Weighting Method for Multisensor Data Fusion." IEEE Sensors Journal 11, no. 9 (September 2011): 1955–61. http://dx.doi.org/10.1109/jsen.2011.2107896.

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31

Motomura, Hideto. "Gamut Mapping using Color-Categorical Weighting Method." Color and Imaging Conference 8, no. 1 (January 1, 2000): 318–23. http://dx.doi.org/10.2352/cic.2000.8.1.art00057.

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32

Ghazali, Alfin, Poltak Sihombing, and Muhammad Zarlis. "Weighting Comparative Analysis Using Fuzzy Logic and Rank Order Centroid (ROC) in the Simple Additive Weighting (SAW) Method." CESS (Journal of Computer Engineering, System and Science) 7, no. 1 (December 27, 2021): 1. http://dx.doi.org/10.24114/cess.v7i1.27758.

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Decision Support System Method which is often referred to as the weighted addition method, one of which is Simple Additive Weighting. But the value of the weights in this system is not officially the calculation used. Therefore, usually a number of researchers combine this method with other methods to be more precise and accurate in supporting their decisions. In this study, the authors compare the results of the SAW method between the weighting based on the Fuzzy Logic method and the weighting based on the Rank Order Centroid (ROC) method. The case studied was the number of student satisfaction with learning outcomes during the Covid-19 pandemic. The results obtained are the number of students who are declared satisfied with learning during the Covid-19 pandemic as many as 6 students for the weighting of the Fuzzy Logic method and 5 students for the weighting of the Rank Order Centroid (ROC) method.

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33

Alsop, Jonathan, Lawrence Pont, and Martin Scott. "VP90 Which Matching Adjusted Indirect Comparison Method Is Best?" International Journal of Technology Assessment in Health Care 35, S1 (2019): 94–95. http://dx.doi.org/10.1017/s0266462319003350.

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IntroductionMatching adjusted indirect comparison (MAIC) methods are extremely useful when conducting ITCs, as they reduce baseline imbalances between studies, particularly upon patient characteristics that are confounded with treatment. The standard approach when conducting MAIC is that proposed by Signorovitch et al. (2010). However, there are newer, and potentially better, methods available.MethodsThree different MAIC methods (Signorovitch, Entropy Balancing, Polynomial Weighting) were compared using multiple phase 3 RCTs conducted in Diabetic Retinal Edema. The matching ability of each method was assessed, alongside its ability to avoid large weights (i.e. avoiding high leverage), and maximise effective same size (ESS). Each method's overall ease of use and impact upon estimates of treatment effectiveness were also evaluated.ResultsAll methods were able to precisely match the aggregate level data. However, the Entropy Balancing and Polynomial Weighting both outperformed the Signorovitch method in terms of having the lowest maximum weights. The Polynomial Weighting provided the highest ESS. The Entropy Balancing method was arguably the most challenging to implement, whilst the Signorovitch method the least. The Polynomial Weighting method appears to provide the greatest flexibility to the user.ConclusionsWhilst the Signorovitch method has become almost synonymous with MAIC, the Entropy Balancing and Polynomial Weighting methods offer potentially superior performance. In the absence of head-to-head trial data, these new MAIC approaches should provide less biased and more precise estimates of comparative effectiveness – ultimately leading to better decision making by regulators and payers.

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34

Prayogi, Satria Yudha. "SIMPLE ADDITIVE WEIGHTING APPLICATION METHOD OF TABLET PC SELECTION FOR BEGINNERS." Computer Engineering, Science and System Journal 1, no. 1 (January 31, 2016): 35–40. http://dx.doi.org/10.24114/cess.v1i1.4034.

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35

Li, Yanjie, Changsheng Cai, and Zhenyu Xu. "A Combined Elevation Angle and C/N0 Weighting Method for GNSS PPP on Xiaomi MI8 Smartphones." Sensors 22, no. 7 (April 6, 2022): 2804. http://dx.doi.org/10.3390/s22072804.

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Traditionally, an elevation-angle-dependent weighting method is usually used for Global Navigation Satellite System (GNSS) positioning with a geodetic receiver. As smartphones adopt linearly polarized antenna and low-cost GNSS chips, different GNSS observation properties are exhibited. As a result, a carrier-to-noise ratio (C/N0)-dependent weighting method is mostly used for smartphone-based GNSS positioning. However, the C/N0 is subject to the effects of the observation environment, resulting in an unstable observation weight. In this study, we propose a combined elevation angle and C/N0 weighting method for smartphone-based GNSS precise point positioning (PPP) by normalizing the C/N0-derived variances to the scale of the elevation-angle-derived variances. The proposed weighting method is validated in two kinematic PPP tests with different satellite visibility conditions. Compared with the elevation-angle-only and C/N0-only weighting methods, the combined weighting method can effectively enhance the smartphone-based PPP accuracy in a three-dimensional position by 22.7% and 24.2% in an open-sky area, and by 52.0% and 26.0% in a constrained visibility area, respectively.

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Pan, Shou Hui, Li Wang, Ying Cheng Xu, and Guo Ping Xia. "Improved Web Text Classification Method for Classifying Quality Safety Accidents." Advanced Materials Research 121-122 (June 2010): 996–1001. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.996.

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Web text classification, as one of the fundamental techniques of web mining, plays an important role in the web mining system. An improved term weighting method is proposed in this paper. Besides term frequency, the location of the term is also considered when calculating the weight of a term. Web pages were divided into 4 text blocks and each text block has its location weight. Experimental result shows that the precision of improved term weighting method is higher than traditional term weighting method.

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37

Yıldırım, Yıldız, and Melek Gülşah Şahin. "How Do Different Weighting Methods Affect the Overall Effect Size in Meta-Analysis? : An Example of Science Attitude in Türkiye Sample." International Journal of Psychology and Educational Studies 10, no. 3 (August 29, 2023): 744–57. http://dx.doi.org/10.52380/ijpes.2023.10.3.1049.

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There is increasing interest in meta-analysis in different fields due to the need to combine the results of primary research. One of the crucial concepts in combining results is weighting. This study examines how Hunter and Schmidt's method, weighting by sample size; Hedges and Vevea's method, weighting by inverse variance; and Osburn and Callender's method, unweighting, affect the overall effect size in meta-analysis. In this context, for meta-analysis, the search was done for studies examining the effects of alternative measurement and assessment techniques and methods in science education on science attitudes. The databases of the HEI National Thesis Center, Web of Science, ERIC, EBSCO, Google Scholar, and DergiPark were searched between 2010 and 2021. Eleven studies (with 14 effect sizes) that met the criteria were included in the meta-analysis. In line with the study's findings, it was observed that the overall effect sizes were significant and did not change much in the weighting methods. Besides, it was found that the method with the lowest standard error was unweighted. The weighting methods of Hunter and Schmidt and Hedges and Vevea gave similar results in terms of standard error. When the correlation coefficient between the weighting methods was examined, it was seen that all correlation coefficients were greater than 0.90.

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38

Ji, Ai Min, Kun Zhu, and Quan Sheng Huang. "Methods Determining the Weights of Characteristics in Mechanical Products Design on Case-Base Reasoning." Applied Mechanics and Materials 138-139 (November 2011): 315–20. http://dx.doi.org/10.4028/www.scientific.net/amm.138-139.315.

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In Case-Based Reasoning (CBR), the determination of the weights of case characteristics is the base of the accurate case match, even influences the CBR execution. The current methods of determining the weights of case characteristics include subjective and objective weighting method but, some defects exist in them. In order to decrease subjectivity and increase objectivity of weight determining, the paper presented a new kind of combination weighting method that is the combination of the Analytic Hierarchy Process (AHP) with roughness set theory. The weighting process about AHP, roughness set theory, and combination method was described in detail respectively. An example was given to verify the effectiveness and accuracy of the combination weighting method.

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39

Sun, Gao Rong. "Stochastic Response Surface Method with Enhanced Weighting Strategy." Applied Mechanics and Materials 224 (November 2012): 272–79. http://dx.doi.org/10.4028/www.scientific.net/amm.224.272.

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The weighted stochastic response surface method (WSRSM) has been demonstrated to be effective in improving the accuracy of the estimation of statistical moments and probability of failure (PoF) upon the stochastic response surface method (SRSM). However, it has been noticed that the weighting method in WSRSM may have little and sometimes negative impact on PoF estimation especially in the cases of low PoF. To address this issue, an enhanced weighting strategy is proposed that the weights of sample points are determined based on their importance not only to regression but also to PoF estimation. Specifically, relatively larger weights are assigned to points closer to the failure surface, which significantly accounts for the accuracy of PoF estimation. Comparative studies show that SRSM with the proposed weighting method outperforms WSRSM producing more accurate PoF estimation without incurring additional function evaluations.

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40

Liu, Xinwang. "Parameterized defuzzification with maximum entropy weighting function—Another view of the weighting function expectation method." Mathematical and Computer Modelling 45, no. 1-2 (January 2007): 177–88. http://dx.doi.org/10.1016/j.mcm.2006.04.014.

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41

Schohl, G. A. "Improved Approximate Method for Simulating Frequency-Dependent Friction in Transient Laminar Flow." Journal of Fluids Engineering 115, no. 3 (September 1, 1993): 420–24. http://dx.doi.org/10.1115/1.2910155.

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A new approximation to the weighting function in Zielke’s (1967) equation is used in an improved implementation of Trikha’s (1975) method for including frequency-dependent friction in transient laminar flow calculations. The new, five-term approximation was fitted to the weighting function using a nonlinear least squares approach. Transient results obtained using the new approximating function are nearly indistinguishable from results obtained using the exact expression for the weighting function.

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42

Nekoui, Mohammad Ali, and Hassan Heidari Jame Bozorgi. "Weighting Matrix Selection Method for LQR Design Based on a Multi-Objective Evolutionary Algorithm." Advanced Materials Research 383-390 (November 2011): 1047–54. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.1047.

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This paper introduces an application of Multi-Objective Evolution Algorithm (MOEA) to design Q and R weighting matrices in Linear Quadratic regulators (LQR). Considering the difficulty of designing weighting matrices for a linear quadratic regulator, a multi-objective evolutionary algorithm based approach is proposed. The LQR weighting matrices, state feedback control rate and consequently the optimal controller are obtained by means of establishing the multi-objective optimization model of LQR weighting matrices and applying MOEA to it, which makes control system meet multiple performance indexes simultaneously. Controller of double inverted pendulum system is designed using the proposed approach. Simulation results show that it has shorter adjusting time and smaller amplitude value deviating from steady-state than a Non-dominated Sorting Genetic Algorithm LQR ( NSGA- LQR )weighting matrices design approach.

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43

Sarvari, Hadi, Daniel W. M. Chan, Behrouz Ashrafi, Timothy O. Olawumi, and Nerija Banaitiene. "Prioritization of Contracting Methods for Water and Wastewater Projects Using the Fuzzy Analytic Hierarchy Process Method." Energies 14, no. 22 (November 22, 2021): 7815. http://dx.doi.org/10.3390/en14227815.

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This study uses the fuzzy analytical hierarchy process (FAHP) method to prioritize contracting methods to determine the most suitable contract option for water and wastewater projects (WWP). Content analysis, a two-round Delphi survey technique, and a series of validation and reliability tests helped establish the 18 key criteria for FAHP analysis. Consequently, data collected from experts through a pairwise comparison questionnaire form the basis for the inputs for the FAHP analysis. Consequently, the final weightings were derived for each of the key criteria and available contracting methods. The results indicate that the bilateral, cooperative, and trilateral contracting methods are the most suitable for WWP in Iran, with the highest weighting. The study provides useful guidance for the top management of project firms in selecting the optimal contracting method for their projects and offers significant contributions from theoretical and practical perspectives.

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44

Seo, Dong-Hun, and Won-Don Lee. "A New Ensemble System using Dynamic Weighting Method." Journal of the Korean Institute of Information and Communication Engineering 15, no. 6 (June 30, 2011): 1213–20. http://dx.doi.org/10.6109/jkiice.2011.15.6.1213.

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45

Qiaoyan Kuang, and Xiaoming Xu. "An Improved Feature Weighting Method for Text Classification." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 3, no. 7 (August 31, 2011): 340–46. http://dx.doi.org/10.4156/aiss.vol3.issue7.40.

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46

Zhang, Lei, and Yu Hang Zhao. "Characteristics Based Adaptive Weighting Method in Ontology Mapping." Advanced Materials Research 121-122 (June 2010): 260–65. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.260.

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Ontology mapping is a crucial task to enable interoperation and interoperability between related ontologies. Combining multiple mapping strategies can improve the mapping accuracy in ontology mapping. However, how to give weight values in multiple strategy ontology mapping is very difficult. Aiming at the problem, characteristics based adaptive weighting method is proposed in the paper. Six similarities is selected to reflect different kinds of ontological information. Ontologies characteristics related to the six similarities, which include self-characteristics and mutual-characteristics of both ontologies, are used to describe both ontologies to be mapped. Weight of each similarity is specified according to the characteristics of both ontologies. Experiment results showed that the method in the paper can get a better effect than traditional weights combining methods.

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47

唐, 新蓉. "Environmental Governance Evaluation Method Based on Regression Weighting." Statistics and Application 10, no. 05 (2021): 816–22. http://dx.doi.org/10.12677/sa.2021.105084.

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48

Vafaei, Nazanin, Rita A. Ribeiro, and Luis M. Camarinha-Matos. "Assessing Normalization Techniques for Simple Additive Weighting Method." Procedia Computer Science 199 (2022): 1229–36. http://dx.doi.org/10.1016/j.procs.2022.01.156.

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Moushuo Wu. "Exploiting Terms Weighting Method for Query Based Summarization." Journal of Convergence Information Technology 7, no. 1 (January 31, 2012): 197–202. http://dx.doi.org/10.4156/jcit.vol7.issue1.24.

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50

Salonen, E. M., R. Kouhia, and R. Holopainen. "Weighting Function Selection Problem in the Galerkin Method." International Journal of Mechanical Engineering Education 41, no. 2 (April 2013): 115–35. http://dx.doi.org/10.7227/ijmee.41.2.5.

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