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  1. 1
    Parameter identification of thermoelectric modules using enhanced slime mould algorithm (ESMA)

    Parameter identification of thermoelectric modules using enhanced slime mould algorithm (ESMA) by Ponnalagu, Dharswini, Mohd Ashraf, Ahmad, Jui, Julakha Jahan

    Published 2024
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  2. 2
    Identification of continuous-time hammerstein system using sine cosine algorithm

    Identification of continuous-time hammerstein system using sine cosine algorithm by E. F., Junis, J. J., Jui, Mohd Helmi, Suid, Mohd Ashraf, Ahmad

    Published 2019
    “…This paper presents the development of identification of continuous-time Hammerstein systems based on Sine Cosine Algorithm (SCA). …”
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  3. 3
    Identification of non-linear dynamic systems using fuzzy system with constrained membership functions

    Identification of non-linear dynamic systems using fuzzy system with constrained membership functions by Yaakob, Mohd. Shafiek

    Published 2004
    “…This study deals with the use of the rule-based fuzzy system for the identification of non-linear dynamic systems. …”
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  4. 4
    Modified multi-verse optimizer for nonlinear system identification of a double pendulum overhead crane

    Modified multi-verse optimizer for nonlinear system identification of a double pendulum overhead crane by Julakha, Jahan Jui, Mohd Ashraf, Ahmad, Muhammad Ikram, Mohd Rashid

    Published 2021
    “…The efficiency of the proposed HMVOSCA algorithm is evaluated using the convergence curve, parameter estimation error, bode plot, function plot, and Wilcoxon's test method. …”
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  5. 5
    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
    “…In particular, the search capacity of the MVO algorithm has been improved using the sine and cosine functions of the Sine Cosine Algorithm (SCA) that will be able to balance the processes of exploration and exploitation. …”
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  6. 6
    Analysis of Toothbrush Rig Parameter Estimation Using Different Model Orders in Real Coded Genetic Algorithm (RCGA)

    Analysis of Toothbrush Rig Parameter Estimation Using Different Model Orders in Real Coded Genetic Algorithm (RCGA) by Ainul, H. M. Y., Salleh, S. M., Halib, N., Taib, H., Fathi, M. S.

    Published 2024
    “…Generation gap used was 0.5 has shorten the algorithm conver-gence time without affecting the model accuracy.…”
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  7. 7
    A hybrid metaheuristic algorithm for identification of continuous-time Hammerstein systems

    A hybrid metaheuristic algorithm for identification of continuous-time Hammerstein systems by Jui, Julakha Jahan, Mohd Ashraf, Ahmad

    Published 2021
    “…This paper presents a new hybrid identification algorithm called the Average Multi-Verse Optimizer and Sine Cosine Algorithm for identifying the continuous-time Hammerstein system. …”
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  8. 8
    Identification of hammerstain model using stochastic perturbation simultaneous approximation

    Identification of hammerstain model using stochastic perturbation simultaneous approximation by Nurriyah, Mohd Noor

    Published 2016
    “…Furthermore, the Identification is done using MATLAB Simulink to simulate the Hammerstein Model. …”
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  9. 9
    Analysis of toothbrush rig parameter estimation using different model orders in Real-Coded Genetic Algorithm (RCGA)

    Analysis of toothbrush rig parameter estimation using different model orders in Real-Coded Genetic Algorithm (RCGA) by Ainul, H. M. Y., Salleh, S. M., Halib, N., Taib, H., Fathi, M. S.

    Published 2018
    “…Generation gap used was 0.5 has shorten the algorithm convergence time without affecting the model accuracy.…”
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  10. 10
    Identification of continuous-time hammerstein model using improved archimedes optimization algorithm

    Identification of continuous-time hammerstein model using improved archimedes optimization algorithm by Islam, Muhammad Shafiqul, Mohd Ashraf, Ahmad, Cho, Bo Wen

    Published 2024
    “…Therefore, this article identified various continuous-time Hammerstein models based on an improved Archimedes optimization algorithm (IAOA) to address these concerns. The proposed algorithm employed two principal modifications to mitigate these issues and enhance identification accuracy: (i) exploration and exploitation phase recalibrations using a revised density decreasing factor and (ii) local optima entrapment alleviation utilizing safe experimentation dynamics. …”
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  11. 11
    Optimization of modified Bouc–Wen model for magnetorheological damper using modified cuckoo search algorithm

    Optimization of modified Bouc–Wen model for magnetorheological damper using modified cuckoo search algorithm by Rosmazi, Rosli, Zamri, Mohamed

    Published 2021
    “…The newly proposed algorithm was tested using a set of standard benchmark functions with different searching space and global optima placement. …”
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  12. 12
    Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh

    Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh by Rawaa Dawoud Hassan, Al-Dabbagh

    Published 2015
    “…ARDE algorithm makes use of JADE strategy and the MDE_pBX parameters adaptive schemes as frameworks. …”
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  13. 13
    Identification of vocal and voice disorders

    Identification of vocal and voice disorders by Murugesa Pandiyan, Paulraj, Prof. Madya Dr., Sazali, Yaacob, Prof. Dr., Mohd Rizon, Mohammed Juhari, Prof. Dr., Sivanandam, S. N., Muthusamy, Hariharan, Dr.

    Published 2011
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  14. 14
    Real-time system identification of an unmanned quadcopter system using fully tuned radial basis function neural networks

    Real-time system identification of an unmanned quadcopter system using fully tuned radial basis function neural networks by Pairan, Mohammad Fahmi, Shamsudin, Syariful Syafiq, Mohd Anwar, Mohd Shazlan

    Published 2021
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  15. 15
    Identification of the thermoelectric cooler using hybrid multi-verse optimizer and sine cosine algorithm based continuous-time Hammerstein model

    Identification of the thermoelectric cooler using hybrid multi-verse optimizer and sine cosine algorithm based continuous-time Hammerstein model by Ahmad, Mohd Ashraf

    Published 2021
    “…This paper presents the identification of the ThermoElectric Cooler (TEC) plant using a hybrid method of Multi-Verse Optimizer with Sine Cosine Algorithm (hMVOSCA) based on continuous-time Hammerstein model. …”
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  16. 16
    Fractional chaos maps with flower pollination algorithm for chaotic systems� parameters identification

    Fractional chaos maps with flower pollination algorithm for chaotic systems� parameters identification by Yousri D., Allam D., Babu T.S., AbdelAty A.M., Radwan A.G., Ramachandaramurthy V.K., Eteiba M.B.

    Published 2023
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  17. 17
    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
    “…It represents an interconnection among neuron which consists of several adjustable parameters which are tuned using a set of learning examples to obtain the desired function represent the actual system. …”
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  18. 18
    Real-time system identification of an unmanned quadcopter system using fully tuned radial basis function neural networks

    Real-time system identification of an unmanned quadcopter system using fully tuned radial basis function neural networks by Pairan, Mohammad Fahmi, Shamsudin, Syariful Syafiq, Yaakub, Mohd Fauzi, Mohd Anwar, Mohd Shazlan

    Published 2021
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  19. 19
    EFFECT OF PENALTY FUNCTION PARAMETER IN OBJECTIVE FUNCTION OF SYSTEM IDENTIFICATION

    EFFECT OF PENALTY FUNCTION PARAMETER IN OBJECTIVE FUNCTION OF SYSTEM IDENTIFICATION by Abd Samad, Md Fahmi

    Published 2013
    “…The search was done using genetic algorithms. A representation of the sensitivity of the penalty function parameter value in model structure selection is given, along with a proposed mathematical function that defines it. …”
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  20. 20
    Effect of Penalty Function Parameter in Objective Function of System Identification

    Effect of Penalty Function Parameter in Objective Function of System Identification by Abd Samad, Md Fahmi, Hishamuddin, Jamaluddin, Robiah, Ahmad, Mohd Shafiek, Yaacob, Abul K. M., Azad

    Published 2013
    “…The search was done using genetic algorithms. A representation of the sensitivity of the penalty function parameter value in model structure selection is given, along with a proposed mathematical function that defines it. …”
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