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Journal articles on the topic 'Weight matrix'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 February 2022

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1

ZHU Qi-dan, 朱齐丹, 孙磊 SUN Lei, and 蔡成涛 CAI Cheng-tao. "Image restoration using adaptive weight matrix." Optics and Precision Engineering 21, no. 6 (2013): 1592–97. http://dx.doi.org/10.3788/ope.20132106.1592.

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2

Parnell, Gregory S., and Timothy E. Trainor. "2.3.1 Using the Swing Weight Matrix to Weight Multiple Objectives." INCOSE International Symposium 19, no. 1 (July 2009): 283–98. http://dx.doi.org/10.1002/j.2334-5837.2009.tb00949.x.

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3

Zheng, W. M. "Relation between weight matrix and substitution matrix: motif search by similarity." Bioinformatics 21, no. 7 (October 28, 2004): 938–43. http://dx.doi.org/10.1093/bioinformatics/bti090.

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4

Dai, Xiao Peng, and Dong Hui Li. "Research on Risk Assessment of Alien Species Based on Group AHP." Advanced Materials Research 765-767 (September 2013): 3094–98. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.3094.

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Alien biological disaster risk assessment is a complicated system engineering. Group decision making method was adopted to determine the alien biological disaster risk assessment index weight. We use group AHP to determine the weight, constructe corresponding judgment matrix of the assessment indexes by experts, calculate the similarity of experts according to the judgment matrix, and then determine the alien biological disaster risk assessment weights through experts weight and the judgment matrix.

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5

Ermagun, Alireza, and David Levinson. "An Introduction to the Network Weight Matrix." Geographical Analysis 50, no. 1 (July 5, 2017): 76–96. http://dx.doi.org/10.1111/gean.12134.

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6

Ummarino, Dario. "Matrix turnover linked to dietary weight loss." Nature Reviews Rheumatology 13, no. 9 (August 10, 2017): 512. http://dx.doi.org/10.1038/nrrheum.2017.137.

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7

Li, Jun, Yongfeng Diao, Mingdong Li, and Xing Yin. "Stability Analysis of Discrete Hopfield Neural Networks with the Nonnegative Definite Monotone Increasing Weight Function Matrix." Discrete Dynamics in Nature and Society 2009 (2009): 1–10. http://dx.doi.org/10.1155/2009/673548.

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The original Hopfield neural networks model is adapted so that the weights of the resulting network are time varying. In this paper, the Discrete Hopfield neural networks with weight function matrix (DHNNWFM) the weight changes with time, are considered, and the stability of DHNNWFM is analyzed. Combined with the Lyapunov function, we obtain some important results that if weight function matrix (WFM) is weakly (or strongly) nonnegative definite function matrix, the DHNNWFM will converge to a stable state in serial (or parallel) model, and if WFM consisted of strongly nonnegative definite function matrix and column (or row) diagonally dominant function matrix, DHNNWFM will converge to a stable state in parallel model.

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8

Tobita, Hidetaka. "General matrix formula for the weight-average molecular weights of crosslinked polymer systems." Journal of Polymer Science Part B: Polymer Physics 36, no. 13 (September 30, 1998): 2423–33. http://dx.doi.org/10.1002/(sici)1099-0488(19980930)36:13<2423::aid-polb17>3.0.co;2-5.

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Li, Li, Renxiang Wang, and Xican Li. "Grey fuzzy comprehensive evaluation of regional financial innovation ability based on two types weights." Grey Systems: Theory and Application 6, no. 2 (August 1, 2016): 187–202. http://dx.doi.org/10.1108/gs-02-2016-0006.

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Purpose – According to the grey uncertainty and the connotation of different types weights, the purpose of this paper is to establish the pattern of multi-dimensional grey fuzzy decision making with feedback based on weight vector and weight matrix, and applies this pattern to evaluate the regional financial innovation ability. Design/methodology/approach – At first, this paper analyzes the connotation of financial innovation ability and establishes the evaluation system of regional financial innovation ability. Second, the formula of computing the multi-objective weighted comprehensive value based on weight vector and weight matrix is put forward. In view of the object function with supervised factor and stability coefficient, this paper gives the formulas to compute weight vector and weight matrix. Moreover, the algorithm of the multi-dimensional grey fuzzy decision making pattern with feedback based on weight vector and weight matrix is expressed. At last, this paper uses the presented pattern to evaluate the financial innovation ability of thirty-one provinces in China. Findings – The results are convincing: the development of regional financial innovation is not balanced in China, having obvious spatial clustering feature. The comparisons of evaluation results based on different forms of weights show that the calculating convergence speed of the pattern presented in this paper is fast. The pattern enhances the rationality of the demarcation point between categories, and the convergence within categories, making the evaluation more reasonable. Practical implications – The method exposed in the paper can be used at evaluating the regional financial innovation ability and even for other similar evaluation problem. Originality/value – The paper succeeds in realising both the pattern of multi-dimensional grey fuzzy decision making with feedback and evaluating the regional financial innovation ability by using the newest developed theories: weighted grey and fuzzy recognition theory based on weight vector and weight matrix.

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10

Chang, Kung-Ching. "Principal eigenvalue for weight matrix in elliptic systems." Nonlinear Analysis: Theory, Methods & Applications 46, no. 3 (October 2001): 419–33. http://dx.doi.org/10.1016/s0362-546x(00)00140-1.

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11

Farotimi, O., A. Dembo, and T. Kailath. "A general weight matrix formulation using optimal control." IEEE Transactions on Neural Networks 2, no. 3 (May 1991): 378–94. http://dx.doi.org/10.1109/72.97914.

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12

Sankowski, Piotr. "Maximum weight bipartite matching in matrix multiplication time." Theoretical Computer Science 410, no. 44 (October 2009): 4480–88. http://dx.doi.org/10.1016/j.tcs.2009.07.028.

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13

Mooijaart, Ab, and P. M. Bentler. "The weight matrix in asymptotic distribution-free methods." British Journal of Mathematical and Statistical Psychology 38, no. 2 (November 1985): 190–96. http://dx.doi.org/10.1111/j.2044-8317.1985.tb00833.x.

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14

Ummarino, Dario. "Correction: Matrix turnover linked to dietary weight loss." Nature Reviews Rheumatology 13, no. 9 (September 2017): 568. http://dx.doi.org/10.1038/nrrheum.2017.143.

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15

Sen, Naresh, Manoj Mishra, Feroz Khan, Abha Meena, and Ashok Sharma. "D-MATRIX: A web tool for constructing weight matrix of conserved DNA motifs." Bioinformation 3, no. 10 (July 27, 2009): 415–18. http://dx.doi.org/10.6026/97320630003415.

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16

Dong, Si Hui, Yu Ku Zhao, and Min Li. "Improvement of Analytic Hierarchy Process and its Application for Coal Mine Safety Assessment." Applied Mechanics and Materials 368-370 (August 2013): 1979–84. http://dx.doi.org/10.4028/www.scientific.net/amm.368-370.1979.

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The calculation of weigh value and consistency of the judgment matrix of analytic hierarchy process (AHP) were studied in this paper. And the study was applied for coal mine safety assessment. Due to the particularity and complexity of the coal mine environment, to make sure the comprehensive assessment for coal mine system safety, analytic hierarchy process was usually used. It is an important problem to solve the weight value and consistency of the judgment matrix when applying analytic hierarchy process. To obtain the weight value and ensure the consistence of the judgment matrix is the main research direction of AHP. The genetic algorithm was applied to calculate the weight value and the consistency of the judgment matrix. And fuzzy comprehensive evaluation method was applied to evaluate the safety condition of the coal mine system with the method of weight value solution in this paper. Case study shows that the calcuation resuts of applying genetic algorithm to calculate idnex weight value is better than additional methods.

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17

Yan, Li Jun, Zong Bin Li, and Xiao Chun Yang. "A Linear Goal Programming Model for Weight Calculation in Fuzzy AHP and its Application in Product Development." Advanced Materials Research 118-120 (June 2010): 712–16. http://dx.doi.org/10.4028/www.scientific.net/amr.118-120.712.

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The key issue of FAHP application is how to derive fuzzy weights from fuzzy pairwise comparison matrix. The most of applications, however, were founding avoiding the use of sophisticated approaches such as fuzzy least squares method and using a simple extent analysis method to derive fuzzy weight from pairwise comparison matrix for the sake of simplicity. But the extent analysis method proves to be incorrect and may lead to a wrong decision result. So, this paper proposes a sound yet simple linear goal programming model to derive weights from pairwise fuzzy comparison matrix, which takes minimizing inconsistence degree of comparison matrix as objective and obtain a normalized weight vector finally. The proposed model is validated by an application to new product development scheme screening decision making.

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18

CHAN, Lai-Wan, and Chi-Cheong SZETO. "WEIGHT GROUPINGS IN SECOND ORDER TRAINING METHODS FOR RECURRENT NETWORKS." International Journal of Neural Systems 11, no. 04 (August 2001): 379–87. http://dx.doi.org/10.1142/s0129065701000849.

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In this paper, we use block-diagonal matrix to approximate the Hessian matrix in the Levenberg Marquardt method during the training of recurrent neural networks. We analyze the weight updating strategies and the groupings of the weights associated with the approximation. Two weight updating strategies, namely asynchronous and synchronous updating methods are investigated. Asynchronous method updated weights of one block at a time while synchronous method updates all weights at the same time. Variations of these two methods, which involve the determination of two parameters μ and λ, are examined. Four weight grouping methods, correlation blocks, k-unit blocks, layer blocks and arbitrary blocks are investigated and compared. Their computational complexity, approximation ability, and training time is analyzed. Comparing with the original Levenberg Marquardt method, the block-diagonal approximation methods give substantial improvement in training time without degrading the generalization ability.

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19

Siddharthan, Rahul. "Dinucleotide Weight Matrices for Predicting Transcription Factor Binding Sites: Generalizing the Position Weight Matrix." PLoS ONE 5, no. 3 (March 22, 2010): e9722. http://dx.doi.org/10.1371/journal.pone.0009722.

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20

Gao, Lu-Hui, Guo-Qing Wang, and Jing Zhang. "Industrial Agglomeration Analysis Based on Spatial Durbin Model: Evidence from Beijing-Tianjin-Hebei Economic Circle in China." Complexity 2021 (July 26, 2021): 1–10. http://dx.doi.org/10.1155/2021/3788784.

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Based on the data of Beijing-Tianjin-Hebei Economic Circle from 2010 to 2019, this paper uses the spatial Durbin model to empirically analyze the impact of financial development and technological innovation on industrial agglomeration. The following are the conclusions of this study: (1) financial development has a positive effect on industrial agglomeration; however, a significant difference exists in the weight effect of the geographic distance matrix compared to the weight of the economic distance matrix; (2) in the spatial Durbin model with two matrix weights, technological innovation has a significant positive effect on industrial agglomeration; and (3) in the spatial Durbin model with two matrix weights, the interaction has a significant negative effect on industrial agglomeration. Therefore, the government should further implement the coordinated development strategy, promoting regional technological innovation for a long time to realize its integration with financial development.

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21

Rodríguez-Alvarez, María-José, Filomeno Sánchez, Antonio Soriano, Amadeo Iborra, and Cibeles Mora. "Exploiting symmetries for weight matrix design in CT imaging." Mathematical and Computer Modelling 54, no. 7-8 (October 2011): 1655–64. http://dx.doi.org/10.1016/j.mcm.2010.12.004.

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22

Fornes, T. D., P. J. Yoon, H. Keskkula, and D. R. Paul. "Nylon 6 nanocomposites: the effect of matrix molecular weight." Polymer 42, no. 25 (December 2001): 09929–40. http://dx.doi.org/10.1016/s0032-3861(01)00552-3.

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23

Savin, D., S. Alkass, and P. Fazio. "Calculating Weight Matrix of Neural Network for Resource Leveling." Journal of Computing in Civil Engineering 12, no. 4 (October 1998): 241–48. http://dx.doi.org/10.1061/(asce)0887-3801(1998)12:4(241).

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24

Scherm, F., R. Völkl, S. van Smaalen, S. Mondal, P. Plamondon, G. L’Espérance, F. Bechmann, and U. Glatzel. "Microstructural characterization of interpenetrating light weight metal matrix composites." Materials Science and Engineering: A 518, no. 1-2 (August 2009): 118–23. http://dx.doi.org/10.1016/j.msea.2009.04.010.

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25

Sha Junchen, Xing Jianping, Gao Liang, and Liang Haozhe. "New Matrix-weight IMM Algorithm Based on Coefficients Filters." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 4, no. 15 (August 31, 2012): 111–18. http://dx.doi.org/10.4156/aiss.vol4.issue15.14.

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26

Munavalli, Girish, Amy Reisenauer, Marsha Moses, Susan Kilroy, and Jack L. Arbiser. "Weight Loss-Induced Calciphylaxis: Potential Role of Matrix Metalloproteinases." Journal of Dermatology 30, no. 12 (December 2003): 915–19. http://dx.doi.org/10.1111/j.1346-8138.2003.tb00348.x.

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27

Wadayama, T. "Average Coset Weight Distribution of Combined LDPC Matrix Ensembles." IEEE Transactions on Information Theory 52, no. 11 (November 2006): 4856–66. http://dx.doi.org/10.1109/tit.2006.883619.

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28

Albalawi, Fahad, Sultan Alshehri, Abderrazak Chahid, and Taous-Meriem Laleg-Kirati. "Voxel Weight Matrix-Based Feature Extraction for Biomedical Applications." IEEE Access 8 (2020): 121451–59. http://dx.doi.org/10.1109/access.2020.3006521.

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29

Claverie, Jean-Michel, and Stéphane Audic. "The statistical significance of nucleotide position-weight matrix matches." Bioinformatics 12, no. 5 (1996): 431–39. http://dx.doi.org/10.1093/bioinformatics/12.5.431.

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30

Mimis, A. "3D WEIGHT MATRICES IN MODELING REAL ESTATE PRICES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W2 (October 6, 2016): 123–25. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w2-123-2016.

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Central role in spatial econometric models of real estate data has the definition of the weight matrix by which we capture the spatial dependence between the observations. The weight matrices presented in literature so far, treats space in a two dimensional manner leaving out the effect of the third dimension or in our case the difference in height where the property resides. To overcome this, we propose a new definition of the weight matrix including the third dimensional effect by using the Hadamard product. The results illustrated that the level effect can be absorbed into the new weight matrix.

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31

Entani, Tomoe. "Two Approximation Models of Fuzzy Weight Vector from a Comparison Matrix." Advances in Fuzzy Systems 2018 (October 24, 2018): 1–9. http://dx.doi.org/10.1155/2018/1975768.

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In this study, our uncertain judgment on multiple items is denoted as a fuzzy weight vector. Its membership function is estimated from more than one interval weight vector. The interval weight vector is obtained from a crisp/interval comparison matrix by Interval Analytic Hierarchy Process (AHP). We redefine it as a closure of the crisp weight vectors which approximate the comparison matrix. The intuitively given comparison matrix is often imperfect so that there could be various approaches to approximate it. We propose two of them: upper and lower approximation models. The former is based on weight possibility and the weight vector with it includes the comparison matrix. The latter is based on comparison possibility and the comparison matrix with it includes the weight vector.

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32

Siddappa, P. N., B. P. Shivakumar, K. B. Yogesha, M. Mruthunjaya, and M. B. Hanamantraygouda. "Machinability study of Al-TiC metal matrix composite." MATEC Web of Conferences 144 (2018): 03001. http://dx.doi.org/10.1051/matecconf/201814403001.

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Aluminum Metal Matrix Composites have emerged as an advanced class of structural materials have a combination of different, superior properties compared to an unreinforced matrix, which can result in a number of service benefits such as increased strength, higher elastic moduli, higher service temperature, low CTE, improved wear resistance, high toughness, etc. The excellent mechanical properties of these materials together with weight saving makes them very attractive for a variety of engineering applications in aerospace, automotive, electronic industries, etc. Hence, these materials provide as alternative substitutes for conventional engineering materials when specific mechanical properties necessary for required applications. In this work an attempt is made to study the machining parameters of Al6061/TiC MMC. The composite is developed by reinforcing TiC particles in varying proportions of 3, 6, 9 and 12 % weight fractions to the Al6061 matric alloy through stir casting technique. Cutting forces were measured by varying cutting speed and feed rate with constant depth of cut for different % weight fractions. The results showed that the cutting force increases with the increase of feed rate and decreases with the increase of cutting speed for all the weight fractions. Cutting parameters were optimized using Taguchi technique.

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33

Masjed-Jamei, Mohammad, and Gradimir Milovanovic. "Construction of Gaussian quadrature formulas for even weight functions." Applicable Analysis and Discrete Mathematics 11, no. 1 (2017): 177–98. http://dx.doi.org/10.2298/aadm1701177m.

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Instead of a quadrature rule of Gaussian type with respect to an even weight function on (?a, a) with n nodes, we construct the corresponding Gaussian formula on (0, a2) with only [(n+1)/2] nodes. Especially, such a procedure is important in the cases of nonclassical weight functions, when the elements of the corresponding three-diagonal Jacobi matrix must be constructed numerically. In this manner, the influence of numerical instabilities in the process of construction can be significantly reduced, because the dimension of the Jacobi matrix is halved. We apply this approach to Pollaczek?s type weight functions on (?1, 1), to the weight functions on R which appear in the Abel-Plana summation processes, as well as to a class of weight functions with four free parameters, which covers the generalized ultraspherical and Hermite weights. Some numerical examples are also included.

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34

Hou, Guan Yu, and De Shan Tang. "Fuzzy Comprehensive Evaluation of Water Resources Carrying Capacity based on Vague Method." Applied Mechanics and Materials 501-504 (January 2014): 2040–44. http://dx.doi.org/10.4028/www.scientific.net/amm.501-504.2040.

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China is a water resource-poor country, in recent years, rapid population growth resulting in water issues have become increasingly prominent. Taking Anhui Province as an example, using fuzzy synthetic evaluation method and combining the analytic hierarchy process evaluation analysis of water resources carrying capacity. Firstly, establishing the evaluation criteria of the index system of regional water resources carrying capacity and individual indicators, calculating evaluation index weight,establishing the weight fuzzy matrix, using Vague set and analytic hierarchy process method to determine the weight vector.Secondly, compositing a fuzzy matrix of weight values after obtaining the value of the weights of the various indicators, calculating the degree of membership, establishing the fuzzy relation matrix, fuzzy subset into a comprehensive evaluation model, and seeking the comprehensive evaluation value .Finally, determining the level of sustainable use of water resources in the specific area based on the principle of maximum degree of membership . After the evaluation are analyzed and compared in order to elaborate the evaluation method is feasible and effective.

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35

Eriksson, Maria, Joris Hamers, Ton Peijs, and Han Goossens. "The Influence of Graft Length and Density on Dispersion, Crystallisation and Rheology of Poly(ε-caprolactone)/Silica Nanocomposites." Molecules 24, no. 11 (June 3, 2019): 2106. http://dx.doi.org/10.3390/molecules24112106.

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Different techniques of grafting polymer chains to filler surfaces are often employed to compatibilise filler and polymer matrices. In this paper the influence of graft length and graft density on the state of dispersion, crystallisation and rheological properties of poly(ε-caprolactone) (PCL)/silica (SiO2) nanocomposites are reported. Grafted silica nanoparticles were prepared through polymerisation of PCL from the nanoparticle surface. Graft length was controlled by the reaction time, while the grafting density was controlled by the monomer-to-initiator ratio. Grafted nanoparticles were mixed with PCL of different molecular weights and the state of dispersion was assessed. Different matrix-to-graft molecular weight ratios resulted in different states of dispersion. Composites based on the higher molecular weight matrix exhibited small spherical agglomerates while the lower molecular weight matrix revealed more sheet-like microstructures. The state of dispersion was found to be relatively independent of graft length and density. Under quiescent conditions the grafts showed increased nucleation ability in the higher molecular weight PCL, while in the lower molecular weight matrix the effect was less pronounced. Rheological experiments showed an increase in viscosity with increased filler content, which was beneficial for the formation of oriented structures in shear-induced crystallisation.

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36

Chan, Jesse. "Weight-adjusted discontinuous Galerkin methods: Matrix-valued weights and elastic wave propagation in heterogeneous media." International Journal for Numerical Methods in Engineering 113, no. 12 (November 28, 2017): 1779–809. http://dx.doi.org/10.1002/nme.5720.

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37

Caria, Cíntia Rabelo e. Paiva, Érica Martins Ferreira Gotardo, Paola Souza Santos, Simone Coghetto Acedo, Thainá Rodrigues de Morais, Marcelo Lima Ribeiro, and Alessandra Gambero. "Extracellular matrix remodeling and matrix metalloproteinase inhibition in visceral adipose during weight cycling in mice." Experimental Cell Research 359, no. 2 (October 2017): 431–40. http://dx.doi.org/10.1016/j.yexcr.2017.08.026.

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38

Zhang, Jian'an, Qi Wang, and Yuan Yuan. "Metric learning by simultaneously learning linear transformation matrix and weight matrix for person re‐identification." IET Computer Vision 13, no. 4 (May 9, 2019): 428–34. http://dx.doi.org/10.1049/iet-cvi.2018.5402.

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39

Ai, Changfa, Shi Qiu, Chunfu Xin, Enhui Yang, and Yanjun Qiu. "Evaluation and optimisation of stone matrix asphalt (SMA-13) mix design using weight-matrix method." Road Materials and Pavement Design 17, no. 4 (February 2016): 958–67. http://dx.doi.org/10.1080/14680629.2015.1137782.

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40

Roy, Roopali, Gwendolyn Louis, Kevin R. Loughlin, Dmitri Wiederschain, Susan M. Kilroy, Carolyn C. Lamb, David Zurakowski, and Marsha A. Moses. "Tumor-Specific Urinary Matrix Metalloproteinase Fingerprinting: Identification of High Molecular Weight Urinary Matrix Metalloproteinase Species." Clinical Cancer Research 14, no. 20 (October 15, 2008): 6610–17. http://dx.doi.org/10.1158/1078-0432.ccr-08-1136.

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41

Nielsen, Morten. "Summation of Multiple Fourier Series in Matrix Weighted -Spaces." Journal of Mathematics 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/135245.

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This paper is concerned with rectangular summation of multiple Fourier series in matrix weighted -spaces. We introduce a product Muckenhoupt condition for matrix weights and prove that rectangular Fourier partial sums converge in the corresponding matrix weighted space , , if and only if the weight satisfies the product Muckenhoupt condition. The same result is shown to hold true for other summation methods such as Cesàro and summation with the Jackson kernel.

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42

Kabir, Akter, Karim, Haque, Rahman, and Sakib. "Development of a Matrix Based Statistical Framework to Compute Weight for Composite Hazards, Vulnerability and Risk Assessments." Climate 7, no. 4 (April 15, 2019): 56. http://dx.doi.org/10.3390/cli7040056.

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Selection of relative weights for different indicators is a critical step during assessment of composite hazards, vulnerability, and risk. While assigning weight to an indicator, it is important to consider the influence of an individual indicator on a particular composite index. In general, the larger the weight of the indicator, the higher the importance of that indicator compared to other indicators. In this study, a new matrix based statistical framework (MSF) for weight assignment is developed that can be considered as the simplest and most accurate method for assigning weights for a large number of indicators. This method (MSF) is based on the valuation of the correlation matrix and Eigenvector associated with Eigenvalue. Relying on the inter build up methodology, MSF can fulfill some built-in gaps among other weightage methods. It can also directly give the ‘decision’ to select the relative weights that are found from the Eigenvector corresponding to the largest Eigenvalue. The new method is applied by assigning weights to 15 socio-economic indicators and assessed vulnerability and risk in the Bangladesh coast. While comparing with other weight methods, it is found that MSF gives the most acceptable physical explanation about the relative values of weights of indicators. In terms of accuracy, MSF is found to be most accurate compared to other weight methods. When large numbers of indicators are involved in an application, MSF is found to be relatively simple and easy to apply compared to other methods.

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43

Luo, Zhi-Quan. "On the Convergence of the LMS Algorithm with Adaptive Learning Rate for Linear Feedforward Networks." Neural Computation 3, no. 2 (June 1991): 226–45. http://dx.doi.org/10.1162/neco.1991.3.2.226.

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We consider the problem of training a linear feedforward neural network by using a gradient descent-like LMS learning algorithm. The objective is to find a weight matrix for the network, by repeatedly presenting to it a finite set of examples, so that the sum of the squares of the errors is minimized. Kohonen showed that with a small but fixed learning rate (or stepsize) some subsequences of the weight matrices generated by the algorithm will converge to certain matrices close to the optimal weight matrix. In this paper, we show that, by dynamically decreasing the learning rate during each training cycle, the sequence of matrices generated by the algorithm will converge to the optimal weight matrix. We also show that for any given ∊ > 0 the LMS algorithm, with decreasing learning rates, will generate an ∊-optimal weight matrix (i.e., a matrix of distance at most ∊ away from the optimal matrix) after O(1/∊) training cycles. This is in contrast to Ω(1/∊log 1/∊) training cycles needed to generate an ∊-optimal weight matrix when the learning rate is kept fixed. We also give a general condition for the learning rates under which the LMS learning algorithm is guaranteed to converge to the optimal weight matrix.

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44

Babu Rao, Jinugu, D. Venkata Rao, G. J. Catherin, and N. R. M. R. Bhargava. "Development of Light Weight AA2024 Alpha Composites." Materials Science Forum 690 (June 2011): 258–61. http://dx.doi.org/10.4028/www.scientific.net/msf.690.258.

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Composites are most promising materials of recent interest. Metal matrix composites (MMCs) possess significantly improved properties compared to unreinforced alloys. There has been an increasing interest in composites containing low density and low cost reinforcements. Hence, composites with fly ash as reinforcement are likely to over come the cost barrier for wide spread applications in automotive and small engine applications. AA 2024 alloy – 2 to 10% (by weight) fly ash composites were made by stir casting route. Phase identification and structural characterization was carried out on fly ash by X-ray diffraction studies. Scanning electron microscopy and optical microscopy was used for microstructure analysis. The hardness, density and compression tests were carried out on all these alloy and composites. The SEM studies reveal that there was a uniform distribution of fly ash particles in the matrix phase and also very good bonding exists between the matrix and reinforcement. With increasing the amount of fly ash the density of the composites was decreased and the hardness was increased. The increase in compression strength was observed with increase in amount of fly ash.

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45

Basso, Antonella, and Stefania Funari. "Introducing Weights Restrictions in Data Envelopment Analysis Models for Mutual Funds." Mathematics 6, no. 9 (September 10, 2018): 164. http://dx.doi.org/10.3390/math6090164.

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Data envelopment analysis has been applied in a number of papers to measure the performance of mutual funds, besides a great many applications on the more diverse fields of performance evaluation. The data envelopment analysis models proposed in the mutual funds literature do not generally set restrictions on the weights assigned to the input and output variables. In this paper, we study the effects of the introduction of different weight restrictions on the results of the performance evaluation of mutual funds. In addition, we provide a unified matrix representation for three widely used approaches on weight restrictions: virtual weight restrictions with constraints on all decision-making units (DMUs) (on all funds); virtual weight restrictions with constraints only on the target unit; assurance regions. Using the unified matrix representation of the weights constraints, we formulate the data envelopment analysis (DEA ) efficiency model and express the efficient frontier in a unified way for the different weight restrictions considered. We investigate the effects of the different weight restrictions on the performance evaluation by means of an empirical application on a set of European mutual funds. Moreover, we study the behaviour of the fund performance scores as the restrictions on the weights become increasingly strict.

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Fu, Yang, Ming Wei, Hai Chuan Zhang, and Liang Gao. "The Fast Diagonal-Matrix-Weight IMM Algorithm for Target Tracking." Advanced Materials Research 461 (February 2012): 132–37. http://dx.doi.org/10.4028/www.scientific.net/amr.461.132.

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The diagonal-matrix-weight IMM (DIMM) algorithm can solve the IMM algorithm confusions of probability density functions (PDFs) and probability masses of stochastic process. Combingandfilter，the Fast-IMM algorithm has a better performance both in accuracy and reducing computational complexity. In order to improve the estimation accuracy and computational complexity，we apply Fast-IMM method to DIMM algorithm. Therefore，A new method, Fast diagonal-matrix-weight IMM (fast-DIMM) algorithm, is proposed in this paper to heighten the real-time application of DIMM algorithm. Simulations indicate that the proposed fast-DIMM algorithm is a competitive alternative algorithm to the IMM algorithm in real time application

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Salehisadaghiani, Farzad, and Lacra Pavel. "Nash Equilibrium Seeking with Non-doubly Stochastic Communication Weight Matrix." EAI Endorsed Transactions on Collaborative Computing 4, no. 13 (June 12, 2020): 158526. http://dx.doi.org/10.4108/eai.13-7-2018.158526.

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VenuDunde and Koteswara Rao NV. "Weight Matrix-Based Representation of Sub-Optimum Disturbance Cancellation Filters." International Journal of Intelligent Systems and Applications 11, no. 10 (October 8, 2019): 15–24. http://dx.doi.org/10.5815/ijisa.2019.10.02.

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Altenaiji, M., Z. W. Guan, W. J. Cantwell, Y. Zhao, and G. K. Schleyer. "Characterisation of aluminium matrix syntactic foams under drop weight impact." Materials & Design 59 (July 2014): 296–302. http://dx.doi.org/10.1016/j.matdes.2014.03.002.

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Viswanath, G., and B. Sundar Rajan. "Matrix characterization of linear codes with arbitrary Hamming weight hierarchy." Linear Algebra and its Applications 412, no. 2-3 (January 2006): 396–407. http://dx.doi.org/10.1016/j.laa.2005.07.008.

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