Log in

Relevant bibliographies by topics / Kernel Least Mean Square (KLMS) / Journal articles

To see the other types of publications on this topic, follow the link: Kernel Least Mean Square (KLMS).

Journal articles on the topic 'Kernel Least Mean Square (KLMS)'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Kernel Least Mean Square (KLMS).'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Chaudhary, Naveed Ishtiaq, Muhammad Asif Zahoor Raja, Junaid Ali Khan, and Muhammad Saeed Aslam. "Identification of Input Nonlinear Control Autoregressive Systems Using Fractional Signal Processing Approach." Scientific World Journal 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/467276.

Full text

Abstract:

A novel algorithm is developed based on fractional signal processing approach for parameter estimation of input nonlinear control autoregressive (INCAR) models. The design scheme consists of parameterization of INCAR systems to obtain linear-in-parameter models and to use fractional least mean square algorithm (FLMS) for adaptation of unknown parameter vectors. The performance analyses of the proposed scheme are carried out with third-order Volterra least mean square (VLMS) and kernel least mean square (KLMS) algorithms based on convergence to the true values of INCAR systems. It is found that

APA, Harvard, Vancouver, ISO, and other styles

2

Wu, Qishuai, Yingsong Li, and Wei Xue. "A Kernel Recursive Maximum Versoria-Like Criterion Algorithm for Nonlinear Channel Equalization." Symmetry 11, no. 9 (2019): 1067. http://dx.doi.org/10.3390/sym11091067.

Full text

Abstract:

In this paper, a kernel recursive maximum Versoria-like criterion (KRMVLC) algorithm has been constructed, derived, and analyzed within the framework of nonlinear adaptive filtering (AF), which considers the benefits of logarithmic second-order errors and the symmetry maximum-Versoria criterion (MVC) lying in reproducing the kernel Hilbert space (RKHS). In the devised KRMVLC, the Versoria approach aims to resist the impulse noise. The proposed KRMVLC algorithm was carefully derived for taking the nonlinear channel equalization (NCE) under different non-Gaussian interferences. The achieved resu

APA, Harvard, Vancouver, ISO, and other styles

3

Chen, Badong, Junli Liang, Nanning Zheng, and José C. Príncipe. "Kernel least mean square with adaptive kernel size." Neurocomputing 191 (May 2016): 95–106. http://dx.doi.org/10.1016/j.neucom.2016.01.004.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Liu, Weifeng, Puskal P. Pokharel, and Jose C. Principe. "The Kernel Least-Mean-Square Algorithm." IEEE Transactions on Signal Processing 56, no. 2 (2008): 543–54. http://dx.doi.org/10.1109/tsp.2007.907881.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Badong Chen, Songlin Zhao, Pingping Zhu, and J. C. Principe. "Quantized Kernel Least Mean Square Algorithm." IEEE Transactions on Neural Networks and Learning Systems 23, no. 1 (2012): 22–32. http://dx.doi.org/10.1109/tnnls.2011.2178446.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Chen, Badong, Songlin Zhao, Pingping Zhu, and José C. Príncipe. "Mean square convergence analysis for kernel least mean square algorithm." Signal Processing 92, no. 11 (2012): 2624–32. http://dx.doi.org/10.1016/j.sigpro.2012.04.007.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

MURUGAN, S. SAKTHIVEL, V. NATARAJAN, and S. RADHA. "ANALYSIS OF MNLMS AND KLMS ALGORITHM FOR UNDERWATER ACOUSTIC COMMUNICATIONS." Fluctuation and Noise Letters 11, no. 04 (2012): 1250023. http://dx.doi.org/10.1142/s021947751250023x.

Full text

Abstract:

The use of adaptive filters to alleviate the degradation caused by wind driven ambient noise in shallow water is considered in this paper. Since, underwater acoustic signals are greatly affected by the ocean interference and ambient noise disturbances when propagating through underwater channels, an effective adaptive filtering system is necessary for denoising the signal which are degraded by noise. Least mean square (LMS), normalized LMS (NLMS), Modified New LMS (MNLMS) and Kalman LMS (KLMS) based adaptive algorithms are analyzed in terms of their performance with the aid of performance meas

APA, Harvard, Vancouver, ISO, and other styles

8

Zhao, Ji, Xiaofeng Liao, Shiyuan Wang, and Chi K. Tse. "Kernel Least Mean Square with Single Feedback." IEEE Signal Processing Letters 22, no. 7 (2015): 953–57. http://dx.doi.org/10.1109/lsp.2014.2377726.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Boloix-Tortosa, Rafael, Juan Jose Murillo-Fuentes, and Sotirios A. Tsaftaris. "The Generalized Complex Kernel Least-Mean-Square Algorithm." IEEE Transactions on Signal Processing 67, no. 20 (2019): 5213–22. http://dx.doi.org/10.1109/tsp.2019.2937289.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Pokharel, Puskal P., Weifeng Liu, and Jose C. Principe. "Kernel least mean square algorithm with constrained growth." Signal Processing 89, no. 3 (2009): 257–65. http://dx.doi.org/10.1016/j.sigpro.2008.08.009.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Zhao, Songlin, Badong Chen, Pingping Zhu, and José C. Príncipe. "Fixed budget quantized kernel least-mean-square algorithm." Signal Processing 93, no. 9 (2013): 2759–70. http://dx.doi.org/10.1016/j.sigpro.2013.02.012.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Murugan, S. Sakthivel, V. Natarajan, and S. Prethivika. "Hardware Implementation of Kalman Least Mean Square-Based Adaptive Algorithm for Denoising Ambient Noises in Shallow Water Region." Fluctuation and Noise Letters 13, no. 03 (2014): 1450018. http://dx.doi.org/10.1142/s0219477514500187.

Full text

Abstract:

Signals transmitted over long distances through underwater acoustic channels are prone to corruption due to wind interference, ambient noises and various other sources of disturbance. Adaptive filters can be used to extenuate the effect of ambient noise in acoustic signals. A competent technique to denoise acoustic signals using adaptive filters has been proposed. Adaptive filtering techniques such as least mean square (LMS), normalized least mean square (NLMS) and Kalman least mean square (KLMS) have been analyzed based on their performance, with the help of characteristics like signal-to-noi

APA, Harvard, Vancouver, ISO, and other styles

13

Fan, Haijin, Qing Song, and Sumit Bam Shrestha. "Online learning with kernel regularized least mean square algorithms." Knowledge-Based Systems 59 (March 2014): 21–32. http://dx.doi.org/10.1016/j.knosys.2014.02.005.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Xu, Xiguang, Hua Qu, Jihong Zhao, Xiaohan Yang, and Badong Chen. "Quantised kernel least mean square with desired signal smoothing." Electronics Letters 51, no. 18 (2015): 1457–59. http://dx.doi.org/10.1049/el.2015.1757.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Wang, Shi-Yuan, Wen-Yue Wang, Lu-Juan Dang, and Yun-Xiang Jiang. "Kernel Least Mean Square Based on the Nyström Method." Circuits, Systems, and Signal Processing 38, no. 7 (2018): 3133–51. http://dx.doi.org/10.1007/s00034-018-1006-2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Wang, Shiyuan, Yunfei Zheng, Shukai Duan, and Lidan Wang. "Simplified quantised kernel least mean square algorithm with fixed budget." Electronics Letters 52, no. 17 (2016): 1453–55. http://dx.doi.org/10.1049/el.2016.1799.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Liu, Yuqi, Yonghui Xu, Jingli Yang, and Shouda Jiang. "A Polarized Random Fourier Feature Kernel Least-Mean-Square Algorithm." IEEE Access 7 (2019): 50833–38. http://dx.doi.org/10.1109/access.2019.2909304.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Wang, Shiyuan, Yunfei Zheng, and Chengxiu Ling. "Regularized Kernel Least Mean Square Algorithm with Multiple-delay Feedback." IEEE Signal Processing Letters 23, no. 1 (2016): 98–101. http://dx.doi.org/10.1109/lsp.2015.2503000.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Lin, Qing, Yulei Huang, and Yan Li. "Density-dependent quantized kernel least mean square with desired smoothing." Journal of Physics: Conference Series 1423 (December 2019): 012068. http://dx.doi.org/10.1088/1742-6596/1423/1/012068.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Liu, Yuqi, Chao Sun, and Shouda Jiang. "A Kernel Least Mean Square Algorithm Based on Randomized Feature Networks." Applied Sciences 8, no. 3 (2018): 458. http://dx.doi.org/10.3390/app8030458.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Parreira, Wemerson D., José Carlos M. Bermudez, Cédric Richard, and Jean-Yves Tourneret. "Stochastic Behavior Analysis of the Gaussian Kernel Least-Mean-Square Algorithm." IEEE Transactions on Signal Processing 60, no. 5 (2012): 2208–22. http://dx.doi.org/10.1109/tsp.2012.2186132.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Sadoghi Yazdi, Hadi, Morteza Pakdaman, and Hamed Modaghegh. "Unsupervised kernel least mean square algorithm for solving ordinary differential equations." Neurocomputing 74, no. 12-13 (2011): 2062–71. http://dx.doi.org/10.1016/j.neucom.2010.12.026.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Zhang, Qiangqiang, and Shiyuan Wang. "Quantised kernel least mean square algorithm with a learning vector strategy." Electronics Letters 56, no. 21 (2020): 1146–47. http://dx.doi.org/10.1049/el.2020.1964.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Garcia-Vega, Sergio, Xiao-Jun Zeng, and John Keane. "Learning from data streams using kernel least-mean-square with multiple kernel-sizes and adaptive step-size." Neurocomputing 339 (April 2019): 105–15. http://dx.doi.org/10.1016/j.neucom.2019.01.055.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Paul, Thomas K., and Tokunbo Ogunfunmi. "Study of the Convergence Behavior of the Complex Kernel Least Mean Square Algorithm." IEEE Transactions on Neural Networks and Learning Systems 24, no. 9 (2013): 1349–63. http://dx.doi.org/10.1109/tnnls.2013.2256367.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Haghighat, Nasser, Mehdi Nouri, Mahrokh G. Shayesteh, and Hashem Kalbkhani. "Variable bit rate video traffic prediction based on kernel least mean square method." IET Image Processing 9, no. 9 (2015): 777–94. http://dx.doi.org/10.1049/iet-ipr.2014.1035.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Liu, Yuqi, Chao Sun, and Shouda Jiang. "A Reduced Gaussian Kernel Least-Mean-Square Algorithm for Nonlinear Adaptive Signal Processing." Circuits, Systems, and Signal Processing 38, no. 1 (2018): 371–94. http://dx.doi.org/10.1007/s00034-018-0862-0.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Zheng, Yunfei, Shiyuan Wang, Jiuchao Feng, and Chi K. Tse. "A modified quantized kernel least mean square algorithm for prediction of chaotic time series." Digital Signal Processing 48 (January 2016): 130–36. http://dx.doi.org/10.1016/j.dsp.2015.09.015.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Khandan, Zahra, and Hadi Sadoghi Yazdi. "A Novel Neuron in Kernel Domain." ISRN Signal Processing 2013 (September 18, 2013): 1–11. http://dx.doi.org/10.1155/2013/748914.

Full text

Abstract:

Kernel-based neural network (KNN) is proposed as a neuron that is applicable in online learning with adaptive parameters. This neuron with adaptive kernel parameter can classify data accurately instead of using a multilayer error backpropagation neural network. The proposed method, whose heart is kernel least-mean-square, can reduce memory requirement with sparsification technique, and the kernel can adaptively spread. Our experiments will reveal that this method is much faster and more accurate than previous online learning algorithms.

APA, Harvard, Vancouver, ISO, and other styles

30

Luo, Xiong, Jing Deng, Ji Liu, Weiping Wang, Xiaojuan Ban, and Jenq-Haur Wang. "A quantized kernel least mean square scheme with entropy-guided learning for intelligent data analysis." China Communications 14, no. 7 (2017): 1–10. http://dx.doi.org/10.1109/cc.2017.8010964.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Li, Kan, and Jose C. Principe. "Transfer Learning in Adaptive Filters: The Nearest Instance Centroid-Estimation Kernel Least-Mean-Square Algorithm." IEEE Transactions on Signal Processing 65, no. 24 (2017): 6520–35. http://dx.doi.org/10.1109/tsp.2017.2752695.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Zhao, Chaochao, Weijie Ren, and Min Han. "Adaptive Sparse Quantization Kernel Least Mean Square Algorithm for Online Prediction of Chaotic Time Series." Circuits, Systems, and Signal Processing 40, no. 9 (2021): 4346–69. http://dx.doi.org/10.1007/s00034-021-01691-z.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Ninisha Nels, S., and J. Amar Pratap Singh. "Hierarchical Fractional Quantized Kernel Least mean Square Filter in Wireless Sensor Network for Data Aggregation." Wireless Personal Communications 120, no. 2 (2021): 1171–92. http://dx.doi.org/10.1007/s11277-021-08509-w.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Eltrass, Ahmed S., Mazhar B. Tayel, and Ahmed F. EL-qady. "Automatic epileptic seizure detection approach based on multi-stage Quantized Kernel Least Mean Square filters." Biomedical Signal Processing and Control 70 (September 2021): 103031. http://dx.doi.org/10.1016/j.bspc.2021.103031.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

KASHANI, HAMIDREZA BARADARAN, HADI SADOGHI YAZDI, and SEYED ALIREZA SEYEDIN. "BACKGROUND ESTIMATION IN KERNEL SPACE." International Journal of Pattern Recognition and Artificial Intelligence 25, no. 01 (2011): 1–35. http://dx.doi.org/10.1142/s0218001411008464.

Full text

Abstract:

One problem in background estimation is the inherent change in the background such as waving tree branches, water surfaces, camera shakes, and the existence of moving objects in every image. In this paper, a new method for background estimation is proposed based on function approximation in kernel domain. For this purpose, Weighted Kernel-based Learning Algorithm (WKLA) is designed. WKLA includes a weighted type of kernel least mean square algorithm with ability to function approximation in the presence of noise. So, the proposed background estimation method includes two stages: firstly, a nov

APA, Harvard, Vancouver, ISO, and other styles

36

محمد, ياسمين عبد الرحمن, та دجلة ابراهيم مهدي. "تقدير دالة الأنحدار اللامعلمي باستخدام بعض الطرائق اللامعلمية الرتيبة". Journal of Economics and Administrative Sciences 14, № 50 (2008): 304. http://dx.doi.org/10.33095/jeas.v14i50.1391.

Full text

Abstract:

This research was concerning to study monotone nonparametric methods for estimating the nonparametric regression function (i.e treatment outlier) to achieve a monotone function (increasing or decreasing). So we will use the monotone methods to treatment outlier but after estimate the regression function with use kernel estimator (Nadarya - Watson) these methods are:- 1- Mukerjee method takes averages of maximums and minimum of subsets of the data was used to adjust the initial kernel regression estimates and use the researcher special case when . 2- Algorithm least square iso

APA, Harvard, Vancouver, ISO, and other styles

37

Vani, H. Y., M. A. Anusuya, and M. L. Chayadevi. "Morlet-Kernel Principal Component Analysis Features for Speech Recognition." Journal of Computational and Theoretical Nanoscience 17, no. 9 (2020): 4482–86. http://dx.doi.org/10.1166/jctn.2020.9102.

Full text

Abstract:

The aim of this paper is to present the application of Morlet wavelet to extract the speech features in place of MFCC features. KPCA is applied for selecting and reducing the large features obtained from Morlet wavelet. NLMS (Normalized Least Mean Square) filter is used to reduce additive noise levels ranging from ±5 dB to ±15 dB. Features are modeled using Ensembled Support Vector Machine classification model for FSDD and Kannada multi speaker data sets. The comparative results are discussed over logistic regression model. The proposed model reduces the noise with 99% of recognition rate for

APA, Harvard, Vancouver, ISO, and other styles

38

Jiaqiang, E., Cheng Qian, Hao Zhu, Qingguo Peng, Wei Zuo, and Guanlin Liu. "Parameter-identification investigations on the hysteretic Preisach model improved by the fuzzy least square support vector machine based on adaptive variable chaos immune algorithm." Journal of Low Frequency Noise, Vibration and Active Control 36, no. 3 (2017): 227–42. http://dx.doi.org/10.1177/0263092317719634.

Full text

Abstract:

In order to solve the hysteretic character of the piezoelectric material for application, the initial weight factors of the hysteretic units are calculated by the Preisach theory and the first-order reversal curves test data, a hysteretic Preisach model based on the improved fuzzy least square support vector machine (improved FLS-SVM) is established. In the established model, the fuzzy least square support vector machine is introduced to calculate more weight factors of the hysteretic units and the adaptive variable chaos immune algorithm is introduced to optimize the penalty factor and the ke

APA, Harvard, Vancouver, ISO, and other styles

39

Chen, Su. "Optimal Bandwidth Selection for Kernel Density Functionals Estimation." Journal of Probability and Statistics 2015 (2015): 1–21. http://dx.doi.org/10.1155/2015/242683.

Full text

Abstract:

The choice of bandwidth is crucial to the kernel density estimation (KDE) and kernel based regression. Various bandwidth selection methods for KDE and local least square regression have been developed in the past decade. It has been known that scale and location parameters are proportional to density functionals∫γ(x)f2(x)dxwith appropriate choice ofγ(x)and furthermore equality of scale and location tests can be transformed to comparisons of the density functionals among populations.∫γ(x)f2(x)dxcan be estimated nonparametrically via kernel density functionals estimation (KDFE). However, the opt

APA, Harvard, Vancouver, ISO, and other styles

40

Qian, Guobing, Dan Luo, and Shiyuan Wang. "Recursive Minimum Complex Kernel Risk-Sensitive Loss Algorithm." Entropy 20, no. 12 (2018): 902. http://dx.doi.org/10.3390/e20120902.

Full text

Abstract:

The maximum complex correntropy criterion (MCCC) has been extended to complex domain for dealing with complex-valued data in the presence of impulsive noise. Compared with the correntropy based loss, a kernel risk-sensitive loss (KRSL) defined in kernel space has demonstrated a superior performance surface in the complex domain. However, there is no report regarding the recursive KRSL algorithm in the complex domain. Therefore, in this paper we propose a recursive complex KRSL algorithm called the recursive minimum complex kernel risk-sensitive loss (RMCKRSL). In addition, we analyze its stabi

APA, Harvard, Vancouver, ISO, and other styles

41

Bongini, Mattia, Massimo Fornasier, Markus Hansen, and Mauro Maggioni. "Inferring interaction rules from observations of evolutive systems I: The variational approach." Mathematical Models and Methods in Applied Sciences 27, no. 05 (2017): 909–51. http://dx.doi.org/10.1142/s0218202517500208.

Full text

Abstract:

In this paper, we are concerned with the learnability of nonlocal interaction kernels for first-order systems modeling certain social interactions, from observations of realizations of their dynamics. This paper is the first of a series on learnability of nonlocal interaction kernels and presents a variational approach to the problem. In particular, we assume here that the kernel to be learned is bounded and locally Lipschitz continuous and that the initial conditions of the systems are drawn identically and independently at random according to a given initial probability distribution. Then th

APA, Harvard, Vancouver, ISO, and other styles

42

Peng, Dan, Yali Liu, Jiasheng Yang, Yanlan Bi, and Jingnan Chen. "Nondestructive Detection of Moisture Content in Walnut Kernel by Near-Infrared Diffuse Reflectance Spectroscopy." Journal of Spectroscopy 2021 (June 16, 2021): 1–9. http://dx.doi.org/10.1155/2021/9986940.

Full text

Abstract:

The rapid and accurate detection of the moisture content is of great significance to the quality evaluation and oil extraction process of walnut kernel. Near-infrared (NIR) spectroscopy is an ideal method for measuring the moisture content in walnut kernel. In this study, a regression model for moisture content in walnut kernel was developed based on NIR diffuse reflectance spectroscopy using chemometric methods. The different spectral pretreatment methods were adopted to preprocess the original spectral data. The whole spectra band was divided into 5 subbands, 10 subbands, 15 subbands, and 20

APA, Harvard, Vancouver, ISO, and other styles

43

Fauzi, Asep Andri, Agus M. Soleh, and Anik Djuraidah. "KAJIAN SIMULASI PERBANDINGAN METODE REGRESI KUADRAT TERKECIL PARSIAL, SUPPORT VECTOR MACHINE, DAN RANDOM FOREST." Indonesian Journal of Statistics and Its Applications 4, no. 1 (2020): 203–15. http://dx.doi.org/10.29244/ijsa.v4i1.610.

Full text

Abstract:

Highly correlated predictors and nonlinear relationships between response and predictors potentially affected the performance of predictive modeling, especially when using the ordinary least square (OLS) method. The simple technique to solve this problem is by using another method such as Partial Least Square Regression (PLSR), Support Vector Regression with kernel Radial Basis Function (SVR-RBF), and Random Forest Regression (RFR). The purpose of this study is to compare OLS, PLSR, SVR-RBF, and RFR using simulation data. The methods were evaluated by the root mean square error prediction (RMS

APA, Harvard, Vancouver, ISO, and other styles

44

Alshammari, Eiman Tamah. "Towards an accurate Ground-Level Ozone Prediction." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 2 (2018): 1131. http://dx.doi.org/10.11591/ijece.v8i2.pp1131-1139.

Full text

Abstract:

This paper motivation is to find the most accurate technique to predict the ground level ozone at Al Jahra station, Kuwait. The data on the meteorological variables (air temperature, relative humidity, solar radiation, direction and speed of wind) and concentration of seven pollutants of environment (SO2, NO2, NO, CO2, CO, NMHC, and CH4) were applied to forecast the ozone concentration in atmosphere. In this report, three methods (PLS regression, support vector machine (SVM), and multiple least-square regression) were used to predict ground-level ozone. We used Fifteen parameters to evaluate t

APA, Harvard, Vancouver, ISO, and other styles

45

Kao, Szu-Pyng, Chao-Nan Chen, Hui-Chi Huang, and Yu-Ting Shen. "Using a least squares support vector machine to estimate a local geometric geoid model." Boletim de Ciências Geodésicas 20, no. 2 (2014): 427–43. http://dx.doi.org/10.1590/s1982-21702014000200025.

Full text

Abstract:

In this study, test-region global positioning system (GPS) control points exhibiting known first-order orthometric heights were employed to obtain the points of plane coordinates and ellipsoidal heights by using the real-time GPS kinematic measurement method. Plane-fitting, second-order curve-surface fitting, back-propagation (BP) neural networks, and least-squares support vector machine (LS-SVM) calculation methods were employed. The study includes a discussion on data integrity and localization, changing reference-point quantities and distributions to obtain an optimal solution. Furthermore,

APA, Harvard, Vancouver, ISO, and other styles

46

Pakdaman, M., Y. Falamarzi, H. Sadoghi Yazdi, A. Ahmadian, S. Salahshour, and F. Ferrara. "A kernel least mean square algorithm for fuzzy differential equations and its application in earth’s energy balance model and climate." Alexandria Engineering Journal 59, no. 4 (2020): 2803–10. http://dx.doi.org/10.1016/j.aej.2020.06.016.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Zhang, Yumei, Xiangying Guo, Xia Wu, Suzhen Shi, and Xiaojun Wu. "RPSOVF Prediction Model for Speech Signal Series Based on UPSO." International Journal of Bifurcation and Chaos 29, no. 06 (2019): 1950075. http://dx.doi.org/10.1142/s0218127419500755.

Full text

Abstract:

In this paper, we propose a nonlinear prediction model of speech signal series with an explicit structure. In order to overcome some intrinsic shortcomings, such as traps at the local minimum, improper selection of parameters, and slow convergence rate, which are always caused by improper parameters generated by, typically, the low performance of least mean square (LMS) in updating kernel coefficients of the Volterra model, a uniform searching particle swarm optimization (UPSO) algorithm to optimize the kernel coefficients of the Volterra model is proposed. The second-order Volterra filter (SO

APA, Harvard, Vancouver, ISO, and other styles

48

Mat Yasin, Zuhaila, Zuhaina Zakaria, and Titik Khawa Abdul Rahman. "Prediction of Undervoltage Load Shedding Using Quantum-Inspired Evolutionary Programming-Support Vector Machine." Applied Mechanics and Materials 785 (August 2015): 43–47. http://dx.doi.org/10.4028/www.scientific.net/amm.785.43.

Full text

Abstract:

This paper presents a new technique to predict the optimal amount of load to be shed at various loading conditions using Quantum-Inspired Evolutionary Programming–Support Vector Machine (QIEP-SVM). QIEP is utilised to optimise the RBF Kernel parameters in Least-Square Support Vector Machine (LS-SVM). The objective of the optimisation is to minimise the mean square error (MSE). The performance of QIEP-SVM technique was compared with those obtained from LS-SVM technique with prediction accuracy through a 10-fold cross-validation procedure. All simulations in this study were carried out using IEE

APA, Harvard, Vancouver, ISO, and other styles

49

Chen, Wei, Himan Shahabi, Shuai Zhang, et al. "Landslide Susceptibility Modeling Based on GIS and Novel Bagging-Based Kernel Logistic Regression." Applied Sciences 8, no. 12 (2018): 2540. http://dx.doi.org/10.3390/app8122540.

Full text

Abstract:

Landslides cause a considerable amount of damage around the world every year. Landslide susceptibility assessments are useful for the mitigation of the associated potential risks to local economic development, land use planning, and decision makers. The main aim of this study was to present a novel hybrid approach of bagging (B)-based kernel logistic regression (KLR), named the BKLR model, for spatial prediction of landslides in the Shangnan County, China. We first selected 15 conditioning factors for landslide susceptibility modeling. Then, the prediction capability of all conditioning factor

APA, Harvard, Vancouver, ISO, and other styles

50

Huang, Min, Weiyan Zhao, Qingguo Wang, Min Zhang, and Qibing Zhu. "Prediction of moisture content uniformity using hyperspectral imaging technology during the drying of maize kernel." International Agrophysics 29, no. 1 (2015): 39–46. http://dx.doi.org/10.1515/intag-2015-0012.

Full text

Abstract:

Abstract Moisture content uniformity is one of critical parameters to evaluate the quality of dried products and the drying technique. The potential of the hyperspectral imaging technique for evaluating the moisture content uniformity of maize kernels during the drying process was investigated. Predicting models were established using the partial least squares regression method. Two methods, using the prediction value of moisture content to calculate the uniformity (indirect) and predicting the moisture content uniformity directly, were investigated. Better prediction results were achieved usi

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!