Search Results - (( using identification system algorithm ) OR ( data optimization method algorithm ))

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  1. 1

    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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  2. 2

    Optimum grouping in a modified genetic algorithm for discrete-time, non-linear system identification by Jamaluddin, Hishamuddin, Abd. Samad, M. F., Ahmad, Robiah, Yaacob, M. S.

    Published 2007
    “…The genetic algorithm approach is widely recognized as an effective and flexible optimization method for system identification. …”
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  3. 3

    Optimum grouping in a modified genetic algorithm for discrete-time, non-linear system identification by Jamaluddin, H., Samad, M. F. A., Ahmad, R., Yaacob, M. S.

    Published 2007
    “…he genetic algorithm approach is widely recognized as an effective and flexible optimization method for system identification. …”
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  4. 4

    Deterministic Mutation-Based Algorithm for Model Structure Selection in Discrete-Time System Identification by Abd Samad, Md Fahmi

    Published 2011
    “…A deterministic mutation-based algorithm is introduced to overcome this problem. Identification studies using NARX (Nonlinear AutoRegressive with eXogenous input) models employing simulated systems and real plant data are used to demonstrate that the algorithm is able to detect significant variables and terms faster and to select a simpler model structure than other well-known EC methods.…”
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  5. 5

    Optimum grouping in a modified genetic algorithm for discrete-time, non-linear system identification by Abd Samad, Md Fahmi

    Published 2007
    “…The genetic algorithm approach is widely recognized as an effective and flexible optimization method for system identification. …”
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  6. 6

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

    Published 2021
    “…his thesis implements a novel nature-inspired metaheuristic optimization algorithm, namely the modified Multi-Verse Optimizer (mMVO) algorithm, to identify the continuous-time model of Hammerstein system. …”
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  7. 7
  8. 8

    A novel single parent mating technique in genetic algorithm for discrete - time system identification by Abd Samad @ Mahmood, Md Fahmi, Zainuddin, Farah Ayiesya, Jamaluddin, Hishamuddin, Azad, Abul K. M.

    Published 2024
    “…In investigating this, four systems of linear and nonlinear classes were simulated to generate discrete-time sets of data i.e. later used for identification. …”
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  9. 9

    Implementation of Evolutionary Algorithms to Parametric Identification of Gradient Flexible Plate Structure by Annisa, Jamali, Muhammad Hasbollah, Hassan, Lidyana, Roslan, Muhamad Sukri, Hadi

    Published 2023
    “…A square aluminium plate experimental rig with a gradient of 30° and all edges clamped were designed and fabricated to acquire input-output vibration data experimentally. This input-output data was then applied in a system identification method, which used an evolutionary algorithm with a linear autoregressive with exogenous (ARX) model structure to generate a dynamic model of the system. …”
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  10. 10

    Implementation of Evolutionary Algorithms to Parametric Identification of Gradient Flexible Plate Structure by Annisa, Jamali, Lidyana, Roslan, Muhammad Hasbollah, Hassan

    Published 2023
    “…A square aluminium plate experimental rig with a gradient of 30° and all edges clamped were designed and fabricated to acquire input-output vibration data experimentally. This input-output data was then applied in a system identification method, which used an evolutionary algorithm with a linear autoregressive with exogenous (ARX) model structure to generate a dynamic model of the system. …”
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  11. 11

    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
    “…System identification is a method to build a model for a dynamic system from the experimental data. …”
    Article
  12. 12

    Single parent mating in genetic algorithm for real robotic system identification by Abd Samad, Md Fahmi, Zainuddin, Farah Ayiesya

    Published 2023
    “…System identification (SI) is a method of determining a mathematical model for a system given a set of input-output data. …”
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  13. 13

    Failure detection analysis of grid-connected photovoltaic systems in tropical climate region / Nurmalessa Muhammad @ Atan by Muhammad @ Atan, Nurmalessa

    Published 2020
    “…A modified threshold-based technique was used to build the FD algorithm based on data from PV system installed at GERC UiTM, Malaysia. …”
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  14. 14

    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
    “…System identification is a method to build a model for a dynamic system from the experimental data. …”
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  15. 15

    Adaptive Fast Orthogonal Search (FOS) algorithm for forecasting streamflow by Osman A., Afan H.A., Allawi M.F., Jaafar O., Noureldin A., Hamzah F.M., Ahmed A.N., El-shafie A.

    Published 2023
    “…Data handling; Forecasting; Nonlinear systems; Regression analysis; Religious buildings; Rivers; Stochastic systems; Stream flow; Fast orthogonal searches; Forecasting accuracy; Forecasting models; High dams; Optimization modeling; Optimization scheme; Pole zero cancellation; Streamflow forecasting; Stochastic models; algorithm; artificial intelligence; hydrological modeling; identification method; optimization; river basin; streamflow; Aswan Dam; Nile River…”
    Article
  16. 16

    A Framework for Green Energy Resources Identification and Integration Supported by Real-Time Monitoring, Control, and Automation Applications by Far Chen, Jong

    Published 2025
    “…The second layer employed spatial data and the fuzzy Technique for Order Preference by Similarity to Ideal Solution algorithm to refine potential solar energy sites, yielding the top 100 optimal locations. …”
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  17. 17

    Improving the modeling capacity of Volterra model using evolutionary computing methods based on Kalman Smoother adaptive filter by ., Edwar Yazid, Mohd Shahir Liew, Setyamartana Parman, Velluruzhati

    Published 2015
    “…The first step is combining the forward and backward estimator in the original Volterra model; the second step is reformulating the Volterra model into a state-space model so that the Kalman Smoother (KS) adaptive filter can be used to estimate the kernel coefficients; the third step is optimization of KS parameters using evolutionary computing algorithms such as particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC). …”
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  18. 18

    Mixed Unscented Kalman Filter and differential evolution for parameter identification by Legowo, Ari, Mohamad, Zahratu H., Park, HoonCheol

    Published 2013
    “…Next, its parameters are identified using mixed Unscented Kalman Filter (UKF) and Differential Evolution (DE) based on the experimental data. DE algorithm is integrated into the UKF algorithm to optimize the Kalman gain obtained. …”
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  19. 19

    Improved intrusion detection algorithm based on TLBO and GA algorithms by Aljanabi, Mohammad, Mohd Arfian, Ismail

    Published 2021
    “…Optimization algorithms are widely used for the identification of intrusion. …”
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  20. 20

    Three phase fault algorithm in distribution system by using database approach and impedance based method by Shamsudin, N.H., Latiff, A.A., Abas, N., Mokhlis, Hazlie, Awalin, L.J.

    Published 2012
    “…A three phase fault location algorithm using database and impedance based method is utilized in distribution system to locate fault which may occur in any possible fault sections and to optimize the switching operations to reduce the outage time affected by fault. …”
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