Search Results - (( parameter optimization system algorithm ) OR ( parameter evaluation step algorithm ))

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  1. 1

    Multiobjective optimization using weighted sum Artificial Bee Colony algorithm for Load Frequency Control by Naidu, K., Mokhlis, Hazlie, Bakar, Ab Halim Abu

    Published 2014
    “…The robustness of the proposed algorithm is further investigated by evaluating the response of the system under simultaneous step load perturbation (SLP), changing load demand and collectively varying system parameters in the range of ±50%. …”
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  2. 2

    A comparative evaluation of PID-based optimisation controller algorithms for DC motor by Ahamed S.R., Parumasivam P., Hossain Lipu M.S., Hannan M.A., Ker P.J.

    Published 2023
    “…Controllers; DC motors; Electric control equipment; Particle swarm optimization (PSO); Proportional control systems; Three term control systems; Two term control systems; Backtracking search algorithms; Comparative analysis; Comparative evaluations; Controller algorithm; Industrial activities; Optimum parameters; Particle swarm optimisation; Proportional integral derivative controllers; Electric machine control…”
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  3. 3

    Fast and optimal tuning of fractional order PID controller for AVR system based on memorizable-smoothed functional algorithm by Ren Hao, Mok, Ahmad, Mohd Ashraf

    Published 2022
    “…The simulations of step response analysis, Bode plot analysis, trajectory tracking analysis, disturbance rejection analysis, and parameter variation analysis are conducted to evaluate the effectiveness of the proposed MSFA-FOPID controller of AVR system. …”
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  4. 4

    Data-driven PID controller of wind turbine systems using safe experimentation dynamics algorithm by Islam, Muhammad Shafiqul, Mohd Ashraf, Ahmad, Mohd Riduwan, Ghazali, Mohd Helmi, Suid, Mohd Zaidi, Mohd Tumari

    Published 2024
    “…Employing a PID-based controller is crucial for wind turbine system performance. Recent interest in optimizing PID control parameters offers advantages in output response enhancement while preserving robustness and simplicity. …”
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  5. 5

    Classification of diabetic retinopathy clinical features using image enhancement technique and convolutional neural network / Abdul Hafiz Abu Samah by Abu Samah, Abdul Hafiz

    Published 2021
    “…Based on investigation different architecture and parameter, the suitable deep learning model has been presented to get optimize best result and testing time. …”
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  6. 6

    An improved marine predators algorithm tuned data-driven multiple-node hormone regulation neuroendocrine-PID controller for multi-input–multi-output gantry crane system by Mohd Zaidi, Mohd Tumari, Mohd Ashraf, Ahmad, Mohd Helmi, Suid, Mohd Riduwan, Ghazali, M Osman, Tokhi

    Published 2023
    “…Undertaking a couple of modifications to the conventional marine predators algorithm (MPA), random average marine predators algorithm (RAMPA) with tunable adaptive coefficient to control the step size (CF) has been proposed in this paper as an enhanced alternative towards fine-tuning data-driven multiple-node hormone regulation neuroendocrine-PID (MnHR-NEPID) controller parameters for the multi-input–multi-output (MIMO) gantry crane system. …”
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  7. 7

    Optimal Tuning of Fractional Order Sliding Mode Controller for PMSM Speed Using Neural Network with Reinforcement Learning by Zahraoui Y., Zaihidee F.M., Kermadi M., Mekhilef S., Alhamrouni I., Seyedmahmoudian M., Stojcevski A.

    Published 2024
    “…To determine the optimal parameters of the FOSMC for control speed in a PMSM drive, a neural network algorithm with reinforcement learning (RLNNA) is proposed. …”
    Article
  8. 8

    Fast PID tuning of AVR system using memory-based smoothed functional algorithm by Islam, Muhammad Shafiqul, Mohd Ashraf, Ahmad, Hao, Mok Ren, Mohd Helmi, Suid, Mohd Zaidi, Mohd Tumari

    Published 2024
    “…Consequently, employing a PID-based controller is crucial for maintaining AVR system performance. Recently, there has been a growing interest in utilizing better optimization tools to tune the PID control parameters, providing an advantage in enhancing AVR system output response while maintaining the robustness and simplicity of PID controller. …”
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  10. 10

    Dynamic control and performance of dual active bridge converter based particle swarm optimization by Suliana, Ab Ghani, Hamdan, Daniyal, Norazila, Jaalam, Norhafidzah, Mohd Saad, Nur Huda, Ramlan

    Published 2022
    “…In direct tuning, the optimal ϕ is directly tuning using PSO. While in indirect tuning method, the optimal values of PI parameters are functioning to well-tuned the desired ϕ in the DAB system, where both values of KP and KI have been optimized using the PSO algorithm. …”
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    Optimization of hydropower reservoir system using genetic algorithm for various climatic scenarios by Tayebiyan, Aida

    Published 2015
    “…In order to increase the system efficiency and maximize the power generation, constructed operation models were optimized. …”
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  13. 13

    Modeling and control of a Pico-satellite attitude using Fuzzy Logic Controller by Zaridah, Mat Zain

    Published 2010
    “…It is observed that the APFLC showed convincing performance over the entire simulation of the Pico-satellite. Genetic Algorithm (GA) is a computational model inspired by evaluation. …”
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  14. 14

    Design of Optimal Pitch Controller for Wind Turbines Based on Back-Propagation Neural Network by Shengsheng, Qin, Zhipeng, Cao, Feng, Wang, Ngu, Sze Song, Kho, Lee Chin, Hui, Cai

    Published 2024
    “…The model is simulated in MATLAB 2019b, real-time data are observed, and the control effect is compared with that of a Takagi–Sugeno optimal controller, firefly algorithm optimal controller and fuzzy controller. …”
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  15. 15

    Time-variant online auto-tuned pi controller using pso algorithm for high accuracy dual active bridge dc-dc converter by Suliana, Ab Ghani, Hamdan, Daniyal, Norazila, Jaalam, Nur Huda, Ramlan, Norhafidzah, Mohd Saad

    Published 2022
    “…This paper proposes a time-variant online auto-tuned PI controller using a particle swarm optimization (PSO) algorithm for the 200 kW DAB system. …”
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  16. 16

    Time-Variant online Auto-Tuned PI controller using PSO algorithm for high accuracy dual active bridge DC-DC converter by Suliana, Ab Ghani, Hamdan, Daniyal, Norazila, Jaalam, Nur Huda, Ramlan, Norhafidzah, Mohd Saad

    Published 2022
    “…This paper proposes a time-variant online auto-tuned PI controller using a particle swarm optimization (PSO) algorithm for the 200 kW DAB system. …”
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  17. 17

    Online auto-tuned proportional-integral controller using particle swarm optimization for dual active bridge DC-DC converter by Suliana, Ab Ghani

    Published 2021
    “…This research also explores the optimization of PI controller parameters in DAB in term of accuracy and dynamic response, where Kp and Ki coefficients were optimized by computational intelligence. …”
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  18. 18

    Development of an islanding detection scheme based on combination of slantlet transform and ridgelet probabilistic neural network in distributed generation by Ahmadipour, Masoud

    Published 2019
    “…In the first step, all possible detection parameters/ signals which can be affected by islanding occurrence in the system are measured locally for islanding and non-islanding conditions. …”
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  19. 19

    Modeling And Optimization Of Lipase-Catalyzed Synthesis Of Adipate Esters Using Response Surface Methodology And Artificial Neural Network by Langroodi, Naz Chaibakhsh

    Published 2010
    “…The synthetic reaction was optimized by Response Surface Methodology (RSM) based on central composite rotatable design (CCRD) to evaluate the interactive effects of reaction parameters including temperature, time, enzyme amount and alcohol/acid molar ratio. …”
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  20. 20

    Application of Machine Learning and Deep Learning Algorithms for Landslide Susceptibility Assessment in Landslide Prone Himalayan Region by Bhattacharya S., Ali T., Chakravortti S., Pal T., Majee B.K., Mondal A., Pande C.B., Bilal M., Rahman M.T., Chakrabortty R.

    Published 2025
    “…We carefully selected relevant metrics based on existing research and conducted a multicollinearity analysis on each parameter to ensure the model?s accuracy. We randomly split the data into training and validation sets in a 70/30 ratio. …”
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