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  1. 1
    Parameter Estimation of Lorenz Attractor: A Combined Deep Neural Network and K-Means Clustering Approach

    Parameter Estimation of Lorenz Attractor: A Combined Deep Neural Network and K-Means Clustering Approach by Nurnajmin Qasrina Ann, ., Pebrianti, Dwi, Mohamad Fadhil, Abas, Bayuaji, Luhur

    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. 2
    A decomposed streamflow non-gradientbased artificial intelligence forecasting algorithm with factoring in aleatoric and epistemic variables / Wei Yaxing

    A decomposed streamflow non-gradientbased artificial intelligence forecasting algorithm with factoring in aleatoric and epistemic variables / Wei Yaxing by Wei , Yaxing

    Published 2024
    “…Consequently, the study involved exploiting optimization techniques to enhance the training artificial intelligence algorithm for streamflow forecasting from a gradient-based to a stochastic population-based approach in several aspects, including solution quality, computational effort, and parameter sensitivity on streanflow in Johor, Malaysia. …”
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  3. 3
    RECURSIVE LEARNING ALGORITHMS ON RBF NETWORKS FOR NONLINEAR SYSTEM IDENTIFICATION

    RECURSIVE LEARNING ALGORITHMS ON RBF NETWORKS FOR NONLINEAR SYSTEM IDENTIFICATION by CATUR ANDRYANI, NUR AFNY

    Published 2010
    “…The finite difference based gradient estimate, proposed in this thesis, provides a viable solution only for identifying a system with irregular sample time.…”
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  4. 4
    Identification of continuous-time model of hammerstein system using modified multi-verse optimizer

    Identification of continuous-time model of hammerstein system using modified multi-verse optimizer by Most. Julakha, Jahan Jui

    Published 2021
    “…The numerical and experimental results also showed that both Hammerstein model subsystems are defined effectively using the mMVO-based method, particularly in quadratic output estimation error and a differentiation parameter index. …”
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  5. 5
    Physics-guided deep neural network to characterize non-Newtonian fluid flow for optimal use of energy resources

    Physics-guided deep neural network to characterize non-Newtonian fluid flow for optimal use of energy resources by Kumar, A., Ridha, S., Narahari, M., Ilyas, S.U.

    Published 2021
    “…However, machine and deep learning methods have higher accuracy but rely heavily on the quality and amount of training data, and the solution may become inconclusive if data is sparse. …”
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  6. 6
    REGION-BASED ADAPTIVE DISTRIBUTED VIDEO CODING CODEC

    REGION-BASED ADAPTIVE DISTRIBUTED VIDEO CODING CODEC by ABDELRAHMAN ELAMIN, ABDELRAHMAN ELAMIN

    Published 2011
    “…However, the rate cannot be exactly calculated at the encoder, instead it can only be estimated. This work also prbposes a feedback-free region-based adaptive DVC solution in pixel domain based on machine learning approach to estimate the side information. …”
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  7. 7
    Adaptive complex neuro-fuzzy inference system for non linear modeling and time series prediction

    Adaptive complex neuro-fuzzy inference system for non linear modeling and time series prediction by Shoorangiz, Mohammadreza

    Published 2013
    “…In this regard, genetic algorithm generates different initial conditions of premise parameters to and the best one. …”
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  8. 8
    Support vector machine and its applications for linear and nonlinear regression in the presence of outliers of high dimensional data

    Support vector machine and its applications for linear and nonlinear regression in the presence of outliers of high dimensional data by Sleabi, Waleed Dhhan

    Published 2016
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  9. 9
    Semiparametric inference procedure for the accelarated failure time model with interval-censored data

    Semiparametric inference procedure for the accelarated failure time model with interval-censored data by Karimi, Mostafa

    Published 2019
    “…A computationally simple two-step iterative algorithm, called estimationapproximation algorithm, is introduced for estimating the parameters of the model on the basis of the rank estimators. …”
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  10. 10
    An Improved Scatter Search Algorithm for Parameter Estimation in Large-Scale Kinetic Models of Biochemical Systems

    An Improved Scatter Search Algorithm for Parameter Estimation in Large-Scale Kinetic Models of Biochemical Systems by Muhammad Akmal, Remli, Mohd Saberi, Mohamad, Safaai, Deris, Sinnott, Richard O., Suhaimi, Napis

    Published 2019
    “…Methods: This paper proposes an improved scatter search algorithm to address the challenging parameter estimation problem. …”
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  11. 11
    An improved scatter search algorithm for parameter estimation in large-scale kinetic models of biochemical systems

    An improved scatter search algorithm for parameter estimation in large-scale kinetic models of biochemical systems by Remli, Muhammad Akmal, Mohamad, Mohd Saberi, Deris, Safaai, Sinnott, Richard, Napis, Suhaimi

    Published 2019
    “…Methods: This paper proposes an improved scatter search algorithm to address the challenging parameter estimation problem. …”
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  12. 12
    The development of parameter estimation method for Chinese hamster ovary model using black widow optimization algorithm

    The development of parameter estimation method for Chinese hamster ovary model using black widow optimization algorithm by Nurul Aimi Munirah, ., Muhammad Akmal, Remli, Noorlin, Mohd Ali, Hui, Wen Nies, Mohd Saberi, Mohamad, Khairul Nizar Syazwan, Wan Salihin Wong

    Published 2020
    “…Metaheuristic parameter estimation is an algorithm framework that is processed using some technique to generate a pattern or graph. …”
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  13. 13
    Parameter Estimation Using Improved Differential Evolution And Bacterial Foraging Algorithms To Model Tyrosine Production In Mus Musculus(Mouse)

    Parameter Estimation Using Improved Differential Evolution And Bacterial Foraging Algorithms To Model Tyrosine Production In Mus Musculus(Mouse) by Jia, Xing Yeoh, Chuii, Khim Chong, Mohd Saberi, Mohamad, Yee, Wen Choon, Lian, En Chai, Safaai, Deris, Zuwairie, Ibrahim

    Published 2015
    “…The hybrid of Differential Evolution algorithm with Kalman Filtering and Bacterial Foraging algorithm is a novel global optimisation method implemented to obtain the best kinetic parameter value. …”
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  14. 14
    Parameter estimation of tapioca starch hydrolysis process: Application of least squares and genetic algorithm

    Parameter estimation of tapioca starch hydrolysis process: Application of least squares and genetic algorithm by Roslina, Rashid, Hishamuddin, Jamaluddin, Nor Aishah, Saidina Amin

    Published 2011
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  15. 15
    An enhanced segment particle swarm optimization algorithm for kinetic parameters estimation of the main metabolic model of Escherichia coli

    An enhanced segment particle swarm optimization algorithm for kinetic parameters estimation of the main metabolic model of Escherichia coli by Mohammed Adam, Kunna, Tuty Asmawaty, Abdul Kadir, Muhammad Akmal, Remli, Noorlin, Mohd Ali, Kohbalan, Moorthy, Noryanti, Muhammad

    Published 2020
    “…In this study, an Enhanced Segment Particle Swarm Optimization algorithm (ESe-PSO) was proposed for kinetic parameters estimation. …”
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  16. 16
    PSO and Linear LS for parameter estimation of NARMAX/NARMA/NARX models for non-linear data / Siti Muniroh Abdullah

    PSO and Linear LS for parameter estimation of NARMAX/NARMA/NARX models for non-linear data / Siti Muniroh Abdullah by Abdullah, Siti Muniroh

    Published 2017
    “…Results suggest that the PSO algorithm is viable alternative to other established algorithms for LLS parameter estimation. …”
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  17. 17
    Parameter estimation of tapioca starch hydrolysis process: application of least squares and genetic algorithm

    Parameter estimation of tapioca starch hydrolysis process: application of least squares and genetic algorithm by Rashid, Roslina, Jamaluddin, Hishamuddin, Saidina Amin, Nor Aishah

    Published 2005
    “…The performance of genetic algorithm (GA) in nonlinear kinetic parameter estimation of topiaca starch hydrolysis was studied and compared with Gauss-Newton method. …”
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  18. 18
    Parameter estimation in double exponential smoothing using genetic algorithm / Foo Fong Yeng, Lau Gee Choon and Zuhaimy Ismail

    Parameter estimation in double exponential smoothing using genetic algorithm / Foo Fong Yeng, Lau Gee Choon and Zuhaimy Ismail by Foo, Fong Yeng, Lau, Gee Choon, Ismail, Zuhaimy

    Published 2014
    “…The objective of this research is to estimate the Double Exponential Smoothing by using Genetic Algorithm Mechanism. …”
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  19. 19
    Estimation in spot welding parameters using genetic algorithm

    Estimation in spot welding parameters using genetic algorithm by Lukman, Hafizi

    Published 2007
    “…By using Genetic algorithm (GA) the spot welding parameters can be estimated.…”
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  20. 20
    Parameter estimation and outlier detection in linear functional relationship model / Adilah Abdul Ghapor

    Parameter estimation and outlier detection in linear functional relationship model / Adilah Abdul Ghapor by Adilah, Abdul Ghapor

    Published 2017
    “…This study begins by proposing a robust technique for estimating the slope parameter in LFRM. In particular, the focus is on the non-parametric estimation of the slope parameter and the robustness of this technique is compared with the maximum likelihood estimation and the Al-Nasser and Ebrahem (2005) method. …”
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