Search Results - (( parameter estimation using algorithm ) OR ( parameter estimation clustering algorithm ))
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1
Parameter Estimation of Lorenz Attractor: A Combined Deep Neural Network and K-Means Clustering Approach
Published 2022“…The most popular method to solve parameter estimation problem is using optimization algorithm that easily trap to local minima and poor in exploitation to find the good solutions. …”
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2
Parameter estimation and outlier detection in linear functional relationship model / Adilah Abdul Ghapor
Published 2017“…As for the multiple outliers, a clustering algorithm is considered and a dendogram to visualise the clustering algorithm is used. …”
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3
Parameter estimation and outlier detection for some types of circular model / Siti Zanariah binti Satari
Published 2015“…Here, we introduce a measure of similarity based on the circular distance and obtain a cluster tree using the single linkage clustering algorithm. …”
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4
A new hybrid deep neural networks (DNN) algorithm for Lorenz chaotic system parameter estimation in image encryption
Published 2023“…The research starts with developing the hybrid deep learning model consisting of DNN and a K-Means Clustering Algorithm. Then, the developed algorithm is implemented to estimate the parameters of the Lorenz system. …”
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5
Parameter estimation of K-distributed sea clutter based on fuzzy inference and Gustafson-Kessel clustering
Published 2011“…This paper proposes a novel approach to estimate the parameters of K-distribution, based on fuzzy Gustafson–Kessel clustering and fuzzy Takagi–Sugeno Kang modelling. …”
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6
An investigation of structural breaks on spot and futures crude palm oil returns
Published 2011“…In contrast to the spot crude palm oil findings, the futures crude palm oil exhibits a lower persistency estimation when structural changes are considered. The results support the importance of structural breaks in this volatility clustering estimation, and failure to do so may lead to bias persistency parameter estimation.…”
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7
The multiple outliers detection for circular univariate data using different agglomerative clustering algorithms
Published 2024“…In univariate circular data, the presence of outliers is acclaimed will affect the parameter estimates and inferences. This study proposes the procedure of detecting multiple outliers, particularly for univariate circular data based on agglomerative clustering algorithms. …”
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8
Kernel and multi-class classifiers for multi-floor wlan localisation
Published 2016“…The multi-class classification strategy is used to ensure quick estimation of the multi-class NN algorithms. …”
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9
Semiparametric binary model for clustered survival data
Published 2014“…We investigated the effects of the strength of cluster correlation and censoring rates on properties of the parameters estimate. …”
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10
Expectation maximization clustering algorithm for user modeling in web usage mining system
Published 2009“…The model is based on expectation-maximization (EM) algorithm and it is used for finding maximum likelihood estimates of parameters in probabilistic models, where the model depends on unobserved latent variables. …”
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11
RECURSIVE LEARNING ALGORITHMS ON RBF NETWORKS FOR NONLINEAR SYSTEM IDENTIFICATION
Published 2010“…The thesis also try to investigate the influence of initialization of RBF weights parameters on the overall learning performance using random method and advanced unsupervised learning, such as clustering techniques, as a comparison. …”
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12
Optimization of ANFIS with GA and PSO estimating α ratio in driven piles
Published 2020“…The system was optimized by changing the number of clusters in the FIS and then the output was used for the GA and PSO optimization algorithm. …”
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13
Machine learning for mapping and forecasting poverty in North Sumatera: a datadriven approach
Published 2024“…The best model was created using the grid search cross-validation, while the best prediction results were created using the RF algorithm, with the following parameters: n-estimator = 50, max depth = 10, min samples split = 2, and min samples leaf = 1. …”
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14
Channel Modeling and Direction-of-Arrival Estimation in Mobile Multiple-Antenna Communication Systems
Published 2005“…Estimated parameters from recent measurements ([PMFOO]) are compared with estimated parameters from model generated waveforms as well as theoretical distribution of received signal's angular spread. …”
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15
Predicting the popularity of tweets using the theory of point processes.
Published 2019“…The mode of the posterior distribution is used as the estimator of the finite-dimensional parameter, and suitable functionals of the predictive distribution for the number of retweets implied by the estimated model are used to predict the tweet popularity. …”
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16
Using Bimodal Gaussian Mixture Model-Based Algorithm for Background Segmentation in Thermal Fever Mass Screening
Published 2011“…To estimate the bi - modal background-foreground distribution mixture parameters, Expectation-Maximization (EM) algorithm is applied and the images are clustered statistically and linearly. …”
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17
EZ-SEP: extended Z-SEP routing protocol with hierarchical clustering approach for wireless heterogeneous sensor network
Published 2021“…In addition, EZ-SEP is weighted up using various estimation schemes such as base station repositioning, altering the field density, and variable nodes energy for comparison with the previous parent algorithm. …”
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18
Trade-off between energy efficiency and collisions for MAC protocols of wireless sensor network
Published 2015“…Secondly, this research introduces a geographical and power based clustering algorithm (GPCA) for WSNs. Trade-off between energy efficiency and collisions in these approaches can be obtained by cluster formation, cluster-head election, data collecting at the cluster-head nodes to reduce data redundancy and thus, save energy. …”
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19
Fault Detection Relevant Modeling of an Industrial Gas Turbine based on Neuro-Fuzzy Approach
Published 2010“…Structure and network weights for the NF model are determined by a synergetic approach – Data clustering and Gradient Descent algorithm. Operation data collected in 10 seconds interval and for one day is used for model training and validation. …”
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20
Time series modeling of water level at Sulaiman Station, Klang River, Malaysia
Published 2010“…The estimation of parameters of the model is accomplished using the hybrid learning algorithm consisting of standard neural network backpropagation algorithm and least squares method. …”
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