Search Results - (( using estimation clustering algorithm ) OR ( parameter optimization method algorithm ))
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1
Parameter Estimation of Lorenz Attractor: A Combined Deep Neural Network and K-Means Clustering Approach
Published 2022“…DNN techniques is suitable in solving nonlinear and complex problem. 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
A new hybrid deep neural networks (DNN) algorithm for Lorenz chaotic system parameter estimation in image encryption
Published 2023“…In addition, the hyperparameter tuning problem is considered in this research to improve the developed hybrid model by using the AOA algorithm. Lastly, a new hybrid technique suggests tackling the current image encryption application problem by using the estimated parameters of chaotic systems with an optimization algorithm, the SKF algorithm. …”
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3
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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4
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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5
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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6
Operational structural damage identification using de-noised modal feature in machine learning / Chen Shilei
Published 2021“…In conclusion, the supervised learning method using FRF change is convenient and effective in identifying the damage state of the plate, and can be optimized through mode shape assessment. …”
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7
Indoor positioning using weighted magnetic field signal distance similarity measure and fuzzy based algorithms
Published 2021“…Therefore, for the second objective, another algorithm named the fuzzy algorithm is designed which combines the clustering algorithm, matching algorithm, triangle area algorithm and average Euclidean algorithm used to estimate location. …”
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8
Fuzzy rank cluster top k Euclidean distance and triangle based algorithm for magnetic field indoor positioning system
Published 2021“…However, the problem of using the KNN algorithm is the fixed K value does not guarantee accurate estimation at every position. …”
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9
Balancing Exploitation And Exploration Search Behavior On Nature-Inspired Clustering Algorithms
Published 2018“…Moreover, the balance between the exploration and exploitation processes in the DPSO framework is considered using a combination of (i) a kernel density estimation technique associated with new bandwidth estimation method and (ii) estimated multi-dimensional gravitational learning coefficients. …”
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10
Statistical data preprocessing methods in distance functions to enhance k-means clustering algorithm
Published 2018“…The K-Means algorithm is the commonest and fast technique in partitional cluster algorithms, although with unnormalized datasets it can achieve local optimal. …”
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11
Chemometric approaches in the evaluation of trace metals in commercially raised tilapia and preliminary health risk assessment of its consumption / Low Kah Hin
Published 2012“…For safety evaluation, the metal concentrations in the edible muscles were compared with the established legal limits and reasonable maximum exposures were simulated using the Monte Carlo algorithm.…”
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12
Harmony Search-Based Fuzzy Clustering Algorithms For Image Segmentation
Published 2011“…Second, a new dynamic HS-based fuzzy clustering algorithm (DCHS) is proposed to automatically estimate the appropriate number of clusters as well as a good fuzzy partitioning of the given dataset. …”
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13
Datasets Size: Effect on Clustering Results
Published 2013“…In this paper, we proposed a research technique that implements descriptive algorithms on numeric datasets of varied sizes. We modeled each subset of our data using EM clustering algorithm; two different numbers of partitions (k) were estimated and used for each experiment. …”
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14
Optimization of turning parameters using genetic algorithm method
Published 2008“…This study about development of optimization for turning parameters based on the Genetic Algorithm (GA). …”
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15
Segmentation of MRI brain images using statistical approaches
Published 2011“…The Gaussian Mixture Model (GMM) is a clustering algorithm that is commonly used for brain MRI segmentation. …”
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16
Parameters optimization of surface grinding process with particles swarm optimization, gravitational search, and sine cosine algorithms: a comparative analysis
Published 2018“…The efficiency of the three algorithms are evaluated and compared with previous results obtained by other optimization methods on similar studies. …”
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17
Optimization of PID parameters for hydraulic positioning system utilizing variable weight Grey-Taguchi and particle swarm optimization
Published 2014“…Particle swarm optimization algorithm (PSO) is one of the artificial intelligence methods. …”
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18
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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19
Optimization of turning parameters using ant colony optimization
Published 2008“…The project objectives are to develop Ant Colony Optimization (ACO) algorithm for CNC turning process and to optimize turning parameters for minimized production cost per unit. …”
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20
Parameter estimation in double exponential smoothing using genetic algorithm / Foo Fong Yeng, Lau Gee Choon and Zuhaimy Ismail
Published 2014“…Trial and error often serves as the best method to determine the parameter. Therefore, a good optimization technique is required for identify the best parameter in minimizing the forecast errors. …”
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