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Artículos de revistas sobre el tema "Linjär algoritm"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 8 de febrero de 2022

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1

Cáceres, José y Alberto Márquez. "A linear algorithm to recognize maximal generalized outerplanar graphs". Mathematica Bohemica 122, n.º 3 (1997): 225–30. http://dx.doi.org/10.21136/mb.1997.126148.

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Amrullah, Muhammad. "PEMODELAN PEMROGRAMAN LINIER DENGAN KOEFISIEN FUNGSI OBJEKTIF, FUNGSI KENDALA DAN VARIABEL KEPUTUSAN BERBENTUK BILANGAN KABUR BESERTA APLIKASINYA". Jurnal Matematika, Statistika dan Komputasi 16, n.º 1 (27 de junio de 2019): 85. http://dx.doi.org/10.20956/jmsk.v16i1.5802.

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Abstrak Dalam penelitian ini penulis akan mengusulkan algoritma untuk memodelkan masalah pemrograman linear kabur dengan bilangan kabur trapesium menggunakan metode simpleks. Secara khusus dalam aplikasi teori ini adalah masalah pengambilan keputusan pemrograman linear kabur dengan menyajikan metode baru untuk menyelesaikan masalah pemrograman linier kabur dengan menggunakan fungsi ranking. Pada dasarnya, langkah-langkah dalam metode penelitian ini sama dengan dengan metode simpleks yang digunakan untuk memecahkan masalah pemrograman linier tegas.Kata kunci: Fuzzy linear programming, fungsi ranking, metode simpleks. AbstractIn this paper author shall propose an algorithm for solving fuzzy linear programming problems with trapezoidal numbers using a simplex method. In particular, an application of this theory in decision making problems is fuzzy linear programming with a new method for solving fuzzy linear programming problems, by use of rank function. Basically, our method is similar to simplex method that was used for solving linear programming problems in crisp environment before.Keywords: Fuzzy linier programming, rank function, simplex method.

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3

Patel, Roshni V. y Jignesh S. Patel. "Optimization of Linear Equations using Genetic Algorithms". Indian Journal of Applied Research 2, n.º 3 (1 de octubre de 2011): 56–58. http://dx.doi.org/10.15373/2249555x/dec2012/19.

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4

PAȘA, Tatiana. "THE GENETIC ALGORITHM FOR SOLVING THE NON-LINEAR TRANSPORTATION PROBLEM". Review of the Air Force Academy 16, n.º 2 (31 de octubre de 2018): 37–44. http://dx.doi.org/10.19062/1842-9238.2018.16.2.4.

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5

Mahapatra, Gautam, Srijita Mahapatra y Soumya Banerjee. "A Study of Firefly Algorithm and its Application in Non-Linear Dynamic Systems". International Journal of Trend in Scientific Research and Development Volume-2, Issue-2 (28 de febrero de 2018): 542–49. http://dx.doi.org/10.31142/ijtsrd8393.

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6

Sharma, Aditya y Er Praveen Kumar Patidar. "Review on Linear Array Antenna with Minimum Side Lobe Level Using Genetic Algorithm". International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (30 de junio de 2018): 2067–70. http://dx.doi.org/10.31142/ijtsrd14544.

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7

Marusenkova, T. "A gating algorithm with reduced computational complexity for linear Kalman filters in embedded systems". Jornal of Kryvyi Rih National University, n.º 50 (2020): 25–31. http://dx.doi.org/10.31721/2306-5451-2020-1-50-25-31.

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8

Han, F., X. Huang, E. Teye, H. Gu, H. Dai y L. Yao. "A nondestructive method for fish freshness determination with electronic tongue combined with linear and non-linear multivariate algorithms". Czech Journal of Food Sciences 32, No. 6 (27 de noviembre de 2014): 532–37. http://dx.doi.org/10.17221/88/2014-cjfs.

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Electronic tongue coupled with linear and non-linear multivariate algorithms was attempted to address the drawbacks of fish freshness detection. Parabramis pekinensis fish samples stored at 4°C were used. Total volatile basic nitrogen (TVB-N) and total viable count (TVC) of the samples were measured. Fisher liner discriminant analysis (Fisher LDA) and support vector machine (SVM) were applied comparatively to classify the samples stored at different days. The results revealed that SVM model was better than Fisher LDA model with a higher identification rate of 97.22% in the prediction set. Partial least square (PLS) and support vector regression (SVR) were applied comparatively to predict the TVB-N and TVC values. The quantitative models were evaluated by the root mean square error of prediction (RMSEP) and the correlation coefficient in the prediction set (R<sub>pre</sub>). The results revealed that SVR model was superior to PLS model with RMSEP = 5.65 mg/100 g, R<sub>pre</sub> = 0.9491 for TVB-N prediction and RMSEP = 0.73 log CFU/g, R<sub>pre</sub> = 0.904 for TVC prediction. This study demonstrated that the electronic tongue together with SVM and SVR has a great potential for a convenient and nondestructive detection of fish freshness.  

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9

Gusti, Kharisma Wiati, Rinda Cahyana y Luthfi Nurwandi. "Simulasi Perhitungan Integral Non Linier Menggunakan Monte Carlo (Studi Kasus Ekonomi Total Biaya)". Jurnal Algoritma 9, n.º 2 (1 de septiembre de 2012): 406–15. http://dx.doi.org/10.33364/algoritma/v.9-2.406.

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10

Mukesh, Anand Mohan Sinha, Kumar. "Algorithm for Linear Programming". IOSR Journal of Mathematics 4, n.º 4 (2012): 48–51. http://dx.doi.org/10.9790/5728-0444851.

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11

Hooker, J. N. "Karmarkar’s Linear Programming Algorithm". Interfaces 16, n.º 4 (agosto de 1986): 75–90. http://dx.doi.org/10.1287/inte.16.4.75.

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12

Imah, Elly Matul, A. R. Hutomo, A. Fitrananda, A. Marshadiany y G. P. Prikarti. "IMPLEMENTASI ALGORITMA INTEGER LINEAR PROGRAMMING UNTUK SISTEM INFORMASI PENJADWALAN RUANGAN DI FAKULTAS ILMU KOMPUTER UNIVERSITAS INDONESIA". Jurnal Sistem Informasi 7, n.º 1 (15 de julio de 2012): 25. http://dx.doi.org/10.21609/jsi.v7i1.291.

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Permasalahan konflik penjadwalan ruangan (timetabling) sering dihadapi hampir sebagian besar institusi akademis di Indonesia, salah satunya di Fakultas Ilmu Komputer Universitas Indonesia (Fasilkom UI). Peningkatan jumlah mahasiswa setiap tahun yang tidak diikuti oleh peningkatan jumlah dan kapasitas kelas menjadi faktor penyebab utama. Selama ini sistem penjadwalan masih dilakukan secara manual, sehingga membutuhkan waktu yang relatif lama dan menyebabkan optimasi pengalokasian kebutuhan ruangan menjadi kurang efisien. Penelitian ini bertujuan untuk menemukan pendekatan yang sesuai dalam menyelesaikan masalah timetabling tersebut. Beberapa pendekatan yang dapat digunakan untuk menyelesaikan masalah ini antara lain algoritma Tabu Search, Simmulated Annealing, Graph Coloring, dan Integer Linear Programming (ILP). Dalam penelitian ini, peneliti menggunakan algoritma ILP karena ILP merupakan model yang paling tepat untuk menyelesaikan masalah timetabling di Fasilkom UI. Algoritma ini dapat meminimalkan waktu yang diperlukan untuk melakukan penjadwalan dari sebulan menjadi hitungan menit. Room scheduling conflict issues (timetabling) are facing most of the academic institutions in Indonesia, one is in the Faculty of Computer Science (Fasilkom) Universitas Indonesia (UI). In the number of students each year followed by no increase in the number and capacity of the class became the main factor. During this scheduling system is still done manually so it takes a relatively long time so that the optimization is less efficient allocation of space requirements. This study aims to find an appropriate approach in solving the timetabling problem. Several approaches can be used to solve these problems include Tabu Search algorithm, Simmulated Annealing, Graph Coloring, and Integer Linear Programming (ILP). In this study we used the ILP algorithm for ILP is the most appropriate model to solve the timetabling problem in Fasilkom UI. This algorithm can minimize the time required to perform the scheduling of a month becomes a matter of minutes.

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13

Zebrowski, Ernest y Leo Takahashi. "Linear regression using a simple linear algorithm". Physics Teacher 25, n.º 5 (mayo de 1987): 320–21. http://dx.doi.org/10.1119/1.2342264.

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14

Prasetiyo, Bayu Adi, Baskoro Rochaddi y Alfi Satriadi. "Aplikasi Citra Sentinel-2 untuk Pemetaan Sebaran Material Padatan Tersuspensi Di Muara Sungai Wulan Demak". Journal of Marine Research 8, n.º 4 (31 de octubre de 2019): 379–86. http://dx.doi.org/10.14710/jmr.v8i4.25193.

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ABSTRAK : Sungai Wulan yang terletak di Kabupaten Demak digunakan masyarakat sebagai jalur keluar dan masuknya kapal nelayan serta saluran pembuangan utama. Hal tersebut berdampak pada besarnya kandungan material padatan tersuspensi yang terdapat pada sungai. Pengamatan sebaran material padatan tersuspensi dibutuhkan untuk mengetahui pola sedimentasi dan penilaian kualitas air. Tujuan dari penelitian ini adalah untuk mengetahui sebaran material padatan tersuspensi di muara Sungai Wulan menggunakan metode pengindraan jauh. Data yang digunakan dalam penelitian ini adalah citra satelit Sentinel-2 dan konsentrasi material padatan tersuspensi hasil uji gravimetri. Pengolahan data menggunakan algoritma Lemigas (1997), Budhiman (2004), Parwati (2006), Laili (2015) dan Metode Regresi (Linear, Eksponensial dan Logaritmik). Hasil analisis data menunjukkan bahwa konsentrasi material padatan tersuspensi di sekitar muara relatif rendah, sedangkan konsentrasi yang tinggi terdapat di arah timur muara sungai atau di daerah yang berupa teluk. Algoritma yang memiliki nilai error terkecil adalah algoritma Metode Regresi dengan MRE sebesar 10,12% dengan nilai RMSE sebesar 4,3493. Berdasarkan hasil yang didapat, dapat disimpulkan jika algoritma Metode Regresi merupakan algoritma yang paling sesuai untuk pemetaan sebaran material padatan tersuspensi di muara Sungai Wulan, Demak. ABSTRACT : The Wulan River which located in Demak Regency is used by people as an entrance point for fishing boats and the main sewers. These have an impact on the amount of total suspended solid contained in the river. Observation about the distribution of total suspended solid is needed to determine sedimentation patterns and water quality assessments. The purpose of this study is to determine the distribution of total suspended solid at the Wulan River estuary using remote sensing method. The data used in this study is Sentinel-2 satellite imagery and the concentration of total suspended solid from gravimetric test. Processing data using Lemigas algorithm (1997), Budhiman (2004), Parwati (2006), Laili (2015) and Regression Method (Linear, Exponential and Logarithm). The results of data analysis show that the total suspended solids distribution at the estuary is relatively low, while the high consentration of total suspended solid is found at the east of the river estuary or in the area that form a bay. Algorithm that has the smallest error value is the regression algorithm with MRE value is 10.12% and RMSE value is 4.3493. Based on the results obtained, it can be concluded that the algorithm with regression method is the most suitable algorithm for mapping the distribution of total suspended solid at the mouth of the Wulan River, Demak.

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15

Pyoung, Chan-kyu y Seung-jun Baek. "Virtual Network Mapping Algorithm for Minimizing Piecewise Linear Cost Function". Journal of Korean Institute of Communications and Information Sciences 41, n.º 6 (30 de junio de 2016): 672–77. http://dx.doi.org/10.7840/kics.2016.41.6.672.

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16

Rukmana, Asep Nana y Bambang Siswoyo. "Penerapan Linier Discriminant Analysis pada Klasifiksi Distress Binsin Perbankan". ETHOS (Jurnal Penelitian dan Pengabdian) 7, n.º 2 (28 de junio de 2019): 224–32. http://dx.doi.org/10.29313/ethos.v7i2.4554.

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Abstract. Linear discrimant machine learning analysis is part of artificial intelligence that can learn from past data, recognize patterns to get optimal solutions. The prediction of the bankruptcy of a Sharia public bank company in Indonesia is very important. Modeling Machine Learning with five input-output models can be implemented between financial ratio variables against bankruptcy. Overall, a linear discrimant analysis algorithm is able to train data to build patterns of input-output relationships and modeling behavior well. Every company certainly wants an appropriate and efficient decision making. Linear discrimant analysis builds predictive models using financial ratio variables as predictors. In this study the model can recognize well the pattern of financial ratios with the results of model validation in the form of means square error 8% and coefficient terminated 98%.Abstrak. Linier discrimant analisys machine learning merupakan bagian dari kecerdasan buatan yang dapat belajar dari data masa lalu, mengenali pola untuk mendapatkan solusi yang optimal. Prediksi kebangkrutan suatu perusahaan bank umum Syariah di Indonesia sangat penting. Modeling Machine Learning dengan lima model input-output dapat diimplementasikan antara variabel rasio keuangan terhadap kebangkrutan. Secara keseluruhan, algoritma linier discrimant analysis mampu melatih data untuk membangun pengenalan pola hubungan input-output dan perilaku pemodelan dengan baik. Setiap perusahaan tentu menginginkan sebuah pengambilan keputusan yang tepat dan efisien. Linier discrimant analisis membangun model prediksi menggunakan variabel rasio keuangan sebagai prediktor. Pada penelitian ini model dapat mengenal dengan baik pengenalan pola rasio keuangan dengan hasil validasi model berupa means square error 8% dan koefesien diterminasi 98%.

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17

B., Lalithadevi. "Novel Technique for Price Prediction by Using Logistic, Linear and Decision Tree Algorithm on Deep Belief Network". International Journal of Psychosocial Rehabilitation 24, n.º 5 (31 de marzo de 2020): 1751–61. http://dx.doi.org/10.37200/ijpr/v24i5/pr201846.

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18

O.O., Zaripov, Shukurova O.P. y Sevinov J.U. "ALGORITHMS FOR IDENTIFICATION OF LINEAR DYNAMIC CONTROL OBJECTS BASED ON THE PSEUDO-CONCEPT CONCEPT". International Journal of Psychosocial Rehabilitation 24, n.º 03 (18 de febrero de 2020): 261–67. http://dx.doi.org/10.37200/ijpr/v24i3/pr200778.

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19

O.O, Zaripov, Shukurova O.P. y Sevinov J.U. "ALGORITHMS FOR IDENTIFICATION OF LINEAR DYNAMIC CONTROL OBJECTS BASED ON THE PSEUDO-CONCEPT CONCEPT". International Journal of Psychosocial Rehabilitation 24, n.º 04 (28 de febrero de 2020): 1981–95. http://dx.doi.org/10.37200/ijpr/v24i4/pr201306.

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20

Arora, Nitin, Garima Bhasin y Neha Sharma. "Two way Linear Search Algorithm". International Journal of Computer Applications 107, n.º 21 (18 de diciembre de 2014): 6–8. http://dx.doi.org/10.5120/19137-9622.

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21

Shi, Shuangning, Yong Shang y Qinglin Liang. "A Novel Linear Beamforming Algorithm". PIERS Online 3, n.º 7 (2007): 1089–92. http://dx.doi.org/10.2529/piers061007112619.

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22

Ižvoltová, Jana y Peter Pisca. "Combinatorial Algorithm In Linear Model". MATEC Web of Conferences 196 (2018): 03017. http://dx.doi.org/10.1051/matecconf/201819603017.

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Gauss-jacobi combinatorial algorithm is an alternative approach to traditional iterative numerical methods, which is primary oriented for parameter estimation in nonlinear models. The combinatorial algorithm is often exploited for outlier diagnosis in nonlinear models, where the other parameter estimation methods lose their efficiency. The paper describes comparison of both of gauss-jacobi combinatorial and gauss-markov models executed on parameter estimation process of levelling network for the reason to find the efficiency of combinatorial algorithm in simply linear model.

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23

Xia, Jie Yun y Shuai Bin Lian. "The Globally Linear Embedding Algorithm". Advanced Materials Research 756-759 (septiembre de 2013): 2682–86. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.2682.

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LLE is a very effective non-linear dimension reduction algorithm and widely explored in machine learning, pattern recognition, data mining and etc. Locally linear, Globally non-linear has always been regarded as the features and advantages of LLE. However, the theoretical derivation presented in this paper shows that when the size of neighborhood is larger than the dimension of the space in which the data are presented, LLE is no longer global nonlinear and almost has the same effect as PCA in dimensionality reduction. At present, a lot of literatures on LLE verify their results on Swiss Roll, Punctured Sphere, Twin Peaks, etc. These manifolds are presented in the three-dimensional Euclidean space and the size of neighborhood is always larger than three to prevent too small to be effective. But in these cases, LLE cannot play its advantage of nonlinearity.

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24

Meyer, Alan. "A linear time Oslo algorithm". ACM Transactions on Graphics 10, n.º 3 (julio de 1991): 312–18. http://dx.doi.org/10.1145/108541.108552.

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25

Munjiza, A., E. Rougier y N. W. M. John. "MR linear contact detection algorithm". International Journal for Numerical Methods in Engineering 66, n.º 1 (2006): 46–71. http://dx.doi.org/10.1002/nme.1538.

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26

Marano, M. y J. N. Ureña. "The linear discrete Polya algorithm". Applied Mathematics Letters 8, n.º 6 (noviembre de 1995): 25–28. http://dx.doi.org/10.1016/0893-9659(95)00080-a.

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27

Hu, Yunfa. "An ordering linear unification algorithm". Journal of Computer Science and Technology 4, n.º 1 (enero de 1989): 23–28. http://dx.doi.org/10.1007/bf02943985.

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28

Holroyd, FC y DC Mason. "Efficient linear quadtree construction algorithm". Image and Vision Computing 8, n.º 3 (agosto de 1990): 218–24. http://dx.doi.org/10.1016/0262-8856(90)90068-g.

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29

SATO, Tadanobu y Keisuke KAJI. "Linear Algorithm to Identify Non-Linear Structural Systems." Doboku Gakkai Ronbunshu, n.º 647 (2000): 155–65. http://dx.doi.org/10.2208/jscej.2000.647_155.

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30

Paredes López, M., A. Meneses Viveros y G. Morales-Luna. "Algoritmo cuántico de Deutsch y Jozsa en GAMA". Revista Mexicana de Física E 64, n.º 2 (11 de junio de 2018): 181. http://dx.doi.org/10.31349/revmexfise.64.181.

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An important feature of quantum computing is its inherent paralellism, allowing to process an exponential number of basic transforms with just a linear number of qubits. The Deutsch-Jozsa algorithm exemplifies the computational complexity reduction. This work reports the implementation and execution of the Deutsch-Josza quantum algoritm in GAMA, a programming language for quantum computing simulation developed by ourselves. Through this simulation, it is possible to explore all the components involved by tracing all the different configurations that each component may take.

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31

Spedicato, Emilio y Maria Teresa Vespucci. "Variations on the Gram-Schmidt and the Huang algorithms for linear systems: A numerical study". Applications of Mathematics 38, n.º 2 (1993): 81–100. http://dx.doi.org/10.21136/am.1993.104537.

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32

Sargent, A. y J. L. Fastook. "A linear algorithm for solving non-linear isothermal ice-shelf equations". Geoscientific Model Development Discussions 7, n.º 2 (18 de marzo de 2014): 1829–64. http://dx.doi.org/10.5194/gmdd-7-1829-2014.

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Abstract. A linear non-iterative algorithm is suggested for solving nonlinear isothermal steady-state Morland–MacAyeal ice shelf equations. The idea of the algorithm is in replacing the problem of solving the non-linear second order differential equations for velocities with a system of linear first order differential equations for stresses. The resulting system of linear equations can be solved numerically with direct methods which are faster than iterative methods for solving corresponding non-linear equations. The suggested algorithm is applicable if the boundary conditions for stresses can be specified. The efficiency of the linear algorithm is demonstrated for one-dimensional and two-dimensional ice shelf equations by comparing the linear algorithm and the traditional iterative algorithm on derived manufactured solutions. The linear algorithm is shown to be as accurate as the traditional iterative algorithm but significantly faster. The method may be valuable as the way to increase the efficiency of complex ice sheet models a part of which requires solving the ice shelf model as well as to solve efficiently two-dimensional ice-shelf equations.

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Bektešević, Dino y Dejan Vinković. "Linear feature detection algorithm for astronomical surveys – I. Algorithm description". Monthly Notices of the Royal Astronomical Society 471, n.º 3 (10 de agosto de 2017): 2626–41. http://dx.doi.org/10.1093/mnras/stx1565.

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Long, Ting, Houjun Wang, Shulin Tian, Jianguo Huang y Bing Long. "Test Generation Algorithm for Linear Systems Based on Genetic Algorithm". Journal of Electronic Testing 26, n.º 4 (8 de junio de 2010): 419–28. http://dx.doi.org/10.1007/s10836-010-5162-y.

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Lipeika, Antanas Leonas. "Formantinių požymių išskyrimo metodai". Informacijos mokslai 42, n.º 43 (1 de enero de 2008): 201–6. http://dx.doi.org/10.15388/im.2008.0.3419.

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Straipsnyje nagrinėjami formantinių požymių išskyrimo metodai. Formantinių požymių išskyrimas remiasi spektro pikų radimu apskaičiuotame iš tiesinės prognozės modelio parametrųų spektre. Formantini ų požymių išskyrimo patikimumas priklauso nuo tiesinės prognozės modelio parametrų vertinimo metodo. Anksčiau tiesinės prognozės modelio parametrų vertinimui naudojome autokoreliacinį metodą, kuris neužtikrindavo patikimo formantinių požymių išskyrimo. Todėl, siekiant padidinti formantini ų požymių išskyrimo patikimumų, ieškoma geresnio tiesinės prognozės modelio parametrų vertinimo metodo. Autokoreliacinis tiesinės prognozės modelio parametrų vertinimo metodas lyginamas su kovariaciniu, Burg, Marple metodais ir modifikuotu Split Levinson algoritmu. Tyrimais nustatyta, kad pagal formančių trajektorijų išskyrimą kovariacinis, Burg, Marple tiesinės prognozės modelio parametrų vertinimo metodai iš esmės nesiskiria nuo autokoreliacinio, o modifikuotu Split Levinson algoritmu gauname daug patikimesnius formančių trajektorijų įverčius.Formant feature extraction methodsAntanas Leonas Lipeika SummaryFormant feature extraction is investigated in the paper. Extraction of formant features is based on calculating frequency positions of spectral peaks. The spectrum has been calculated from parameters of linear prediction model. Reliability of formant feature extraction depends on the method used for linear prediction model parameter estimation. The autocorrelation method previously used for linear prediction model parameter estimation was not reliable enough for formant feature extraction. Therefore we were looking for more reliable method of linear prediction model parameter estimation. The previously used autocorrelation method was compared with covariance, Burg, Marple methods and the modified Split Levinson algorithm. It was concluded, that autocorrelation, covariance, Burg and Marple methods are similar from the point of view of formant feature extraction. The modified Split Levinson algorithm provides the best formant feature estimates.

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Škrabánek, Pavel y Natália Martínková. "Algorithm 1017". ACM Transactions on Mathematical Software 47, n.º 3 (25 de junio de 2021): 1–18. http://dx.doi.org/10.1145/3451389.

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Fuzzy regression provides an alternative to statistical regression when the model is indefinite, the relationships between model parameters are vague, the sample size is low, or the data are hierarchically structured. Such cases allow to consider the choice of a regression model based on the fuzzy set theory. In fuzzyreg, we implement fuzzy linear regression methods that differ in the expectations of observational data types, outlier handling, and parameter estimation method. We provide a wrapper function that prepares data for fitting fuzzy linear models with the respective methods from a syntax established in R for fitting regression models. The function fuzzylm thus provides a novel functionality for R through standardized operations with fuzzy numbers. Additional functions allow for conversion of real-value variables to be fuzzy numbers, printing, summarizing, model plotting, and calculation of model predictions from new data using supporting functions that perform arithmetic operations with triangular fuzzy numbers. Goodness of fit and total error of the fit measures allow model comparisons. The package contains a dataset named bats with measurements of temperatures of hibernating bats and the mean annual surface temperature reflecting the climate at the sampling sites. The predictions from fuzzy linear models fitted to this dataset correspond well to the observed biological phenomenon. Fuzzy linear regression has great potential in predictive modeling where the data structure prevents statistical analysis and the modeled process exhibits inherent fuzziness.

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Park, Ji-Hun, Kyung-Won Min y Hongjin Kim. "Probabilistic bounded non-linear control algorithm for linear structures". Probabilistic Engineering Mechanics 20, n.º 2 (abril de 2005): 168–78. http://dx.doi.org/10.1016/j.probengmech.2005.05.001.

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Buckley, James J. y Thomas Feuring. "Linear and non-linear fuzzy regression: Evolutionary algorithm solutions". Fuzzy Sets and Systems 112, n.º 3 (junio de 2000): 381–94. http://dx.doi.org/10.1016/s0165-0114(98)00154-7.

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39

Nugroho, Hapsoro Agung y Haryas Subyantara Wicaksana. "Acceleration classification of earthquake signals and linear signals of human activity using smartphone accelerometer based on support vector machine algorithm". Jurnal Teknologi dan Sistem Komputer 7, n.º 4 (7 de octubre de 2019): 166–71. http://dx.doi.org/10.14710/jtsiskom.7.4.2019.166-171.

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Resumen

The threat of earthquake calamity spread throughout most of the Indonesian archipelago. Smartphone’s accelerometer usage as a seismic parameter detector in Indonesia, of which the noise has obstacles, mainly due to human activities. This study aims to classify linear acceleration signals caused by human activity and earthquake acceleration signals as an initial effort to reduce noise caused by human activity in the smartphone’s accelerometer signals. Both signals are classified by using the Support Vector Machine (SVM) algorithm of which consists of several steps, respectively, data collection, data preprocessing, data segmentation, feature extraction, and classification. These algorithms are tested to 2545 human activity signals in trouser pocket, 2430 human activity signals in shirt pocket and earthquake acceleration signals. Based on the test results by using the confusion matrix, linear acceleration signal data caused by human activity and earthquake acceleration signals can be classified properly using an SVM algorithm with Polynomial or Gaussian kernel with a small kernel scale value. The algorithms can achieve an accuracy of 87.74% to 97.94%.

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40

Yan, YANG, LI Yifei y YUE Hui. "Adaptive linear transmission estimation dehazing algorithm". Journal of Applied Optics 40, n.º 3 (2019): 447–53. http://dx.doi.org/10.5768/jao201940.0302007.

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41

Pan, Pingqi. "A Fast Algorithm for Linear Programming". Journal of Computational Mathematics 28, n.º 6 (junio de 2010): 837–47. http://dx.doi.org/10.4208/jcm.3105-m2897.

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42

Chang, Ya-qin y Chen-hui Jin. "Linear Distinguishing Attack on Shannon Algorithm". Journal of Electronics & Information Technology 33, n.º 1 (28 de febrero de 2011): 190–93. http://dx.doi.org/10.3724/sp.j.1146.2009.01626.

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43

Gao Junwen y Quan Yanming. "Adaptive Linear of Blind Separation Algorithm". INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 5, n.º 7 (15 de abril de 2013): 1223–29. http://dx.doi.org/10.4156/aiss.vol5.issue7.145.

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44

Kaufman, Edwin H. y Terry D. Lenker. "Linear Convergence and the Bisection Algorithm". American Mathematical Monthly 93, n.º 1 (enero de 1986): 48. http://dx.doi.org/10.2307/2322546.

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45

Kim, Kwang-Y. y Gerald R. North. "EOF-Based Linear Prediction Algorithm: Theory". Journal of Climate 11, n.º 11 (noviembre de 1998): 3046–56. http://dx.doi.org/10.1175/1520-0442(1998)011<3046:eblpat>2.0.co;2.

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46

Kim, Kwang-Y. y Gerald R. North. "EOF-Based Linear Prediction Algorithm: Examples". Journal of Climate 12, n.º 7 (julio de 1999): 2076–92. http://dx.doi.org/10.1175/1520-0442(1999)012<2076:eblpae>2.0.co;2.

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47

Kaufman, Edwin H. y Terry D. Lenker. "Linear Convergence and the Bisection Algorithm". American Mathematical Monthly 93, n.º 1 (enero de 1986): 48–51. http://dx.doi.org/10.1080/00029890.1986.11971744.

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48

Peng, Shige. "A linear approximation algorithm using BSDE". Pacific Economic Review 4, n.º 3 (octubre de 1999): 285–92. http://dx.doi.org/10.1111/1468-0106.00079.

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49

Higgs, Russell. "Revisiting the linear information flow algorithm". International Journal of Information and Coding Theory 2, n.º 2/3 (2013): 96. http://dx.doi.org/10.1504/ijicot.2013.059703.

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50

D’alessandro, P. "A primal conical linear programming algorithm". Optimization 25, n.º 2-3 (enero de 1992): 197–207. http://dx.doi.org/10.1080/02331939208843819.

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