Search Results - (( parameter evaluation method algorithm ) OR ( variable simulation model algorithm ))

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  1. 1

    Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm by Manoharan P., Ravichandran S., Kavitha S., Tengku Hashim T.J., Alsoud A.R., Sin T.C.

    Published 2025
    “…The orthogonal learning mechanism improves the performance of the original GOOSE algorithm. This FC model uses the root mean squared error as the objective function for optimizing the unknown parameters. …”
    Article
  2. 2

    A firefly algorithm based hybrid method for structural topology optimization by Gebremedhen, H.S., Woldemichael, D.E., Hashim, F.M.

    Published 2020
    “…In this paper, a firefly algorithm based hybrid algorithm through retaining global convergence of firefly algorithm and ability to generate connected topologies of optimality criteria (OC) method is proposed as an alternative method to solve stress-based topology optimization problems. …”
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    Article
  3. 3

    Hybrid optimization approach to estimate random demand by Wahab, Musa, Ku-Mahamud, Ku Ruhana, Yasin, Azman

    Published 2012
    “…The main objective of this study is to develop a demand forecasting model that should reflect the characteristics of random demand patterns.To accomplish this goal, a hybrid algorithm combining a genetic algorithm and a local search algorithm method was developed to overcome premature convergence in local optima problems.The performance of the hybrid algorithm was compared with a single algorithm model in estimating parameter values that minimize objective function which was used to measure the goodness-of-fit between the observed data and simulated results.However, two problems had to be overcome in the forecasting random demand model. …”
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    Conference or Workshop Item
  4. 4

    Neural network based adaptive pid controller for shell-and-tube heat exchanger by Othman, Mohamad Hakimi

    Published 2019
    “…Dynamic time series neural network model was used together with Levenberg-Marquardt algorithm as the training method. …”
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    Student Project
  5. 5

    Neural network based adaptive pid controller for shell-and-tube heat exchanger: article by Othman, Mohamad Hakimi

    Published 2019
    “…Dynamic time series neural network model was used together with Levenberg-Marquardt algorithm as the training method. …”
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    Article
  6. 6

    Numerical Analysis of structural batteries response with the presence of uncertainty by Syahiir Kamil, Ahmad Kamal Ariffin, Abdul Hadi Azman, Mohamad Syazwan Zafwan Mohamad Suffian

    Published 2023
    “…In evaluating the influence of the uncertainty parameters, Interval Monte Carlo Simulation and the interval finite element method are used to compute the bounds of the structure behavior. …”
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    Article
  7. 7

    SWAT and ANN model hydrological assessment using Malaysia soil data / Khairi Khalid by Khalid, Khairi

    Published 2017
    “…SWAT-CUP, which links SUFI-2 algorithm to SWAT models, has been utilized in the study for the calibration of SWAT models. …”
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    Thesis
  8. 8

    Numerical Analysis of Structural Batteries Response with the Presence of Uncertainty by Syahiir, Kamil, Mohamad Syazwan Zafwan, Mohamad Suffian, Ahmad Kamal Ariffin, Mohd Ihsan, Abdul Hadi, Azman

    Published 2023
    “…In evaluating the influence of the uncertainty parameters, Interval Monte Carlo Simulation and the interval finite element method are used to compute the bounds of the structure behavior. …”
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    Article
  9. 9

    Evaluation of lightning return stroke current using measured electromagnetic fields by Mahdi, Izadi

    Published 2012
    “…Moreover, the current wave shapes are validated with measured field at other observation point when the determined current parameters and the proposed direct procedure algorithms are applied. …”
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    Thesis
  10. 10

    Automated calibration of baseline model for energy conservation using multi-objective Evolutionary Programming (EP) / Ahmad Amiruddin Mohammad Aris by Mohammad Aris, Ahmad Amiruddin

    Published 2019
    “…The proposed co-simulation process is developed by coupling building energy simulation (BES) software, Energy Plus with multi-objective evolutionary programming (MOEP) algorithm which is implemented in Matlab using coupling software, BCVTB. …”
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    Thesis
  11. 11

    Potential Evapotranspiration Estimation Methods for Water Balance Analysis Using SWAT: A Case Study of Kelantan River Basin, Kelantan by Husain M.K., Hayder G., Mohd Sidek L., Ahmed A.N., Kushiar K.F.

    Published 2023
    “…The model calibration applied for the data set from 1985 to 2000 by utilising the SUFI-2 algorithm and validated for the period from 2001 to 2016 with three different PET methods: P-M, P-T, and HG. …”
    Book Chapter
  12. 12

    Power prediction using the wind turbine power curve and data-driven approaches / Ehsan Taslimi Renani by Ehsan Taslimi , Renani

    Published 2018
    “…To evaluate the performance of the Weibull parameters’ estimator methods, two sets of data are considered, one based on simulated data with different random variable size and the other based on actual data collected from a wind farm in Iran. …”
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    Thesis
  13. 13

    A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models by ul Islam, B., Baharudin, Z.

    Published 2017
    “…Genetic algorithm and simulated annealing techniques are used to optimize the control parameters of the neural network. …”
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    Article
  14. 14

    Assessing the simulation performances of multiple model selection algorithm by Yusof, Norhayati, Ismail, Suzilah, Tuan Muda, Tuan Zalizam

    Published 2015
    “…The capability of the algorithm in finding the true specification of multiple models is measured by the percentage of simulation outcomes.Overall results show that the algorithm has performed well for a model with two equations.The findings also indicated that the number of variables in the true models affect the algorithm performances. …”
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    Conference or Workshop Item
  15. 15

    Forecasting hydrological parameters for reservoir system utilizing artificial intelligent models and exploring their influence on operation performance by Allawi, Mohammed Falah, Jaafar, Othman, Mohamad Hamzah, Firdaus, Koting, Suhana, Mohd, Nuruol Syuhadaa, El-Shafie, Ahmed

    Published 2019
    “…During the simulation procedure, although reservoir inflow and evaporation are stochastic variables that are required to be forecasted during simulation, they are considered deterministic variables. …”
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    Article
  16. 16

    SURE-Autometrics algorithm for model selection in multiple equations by Norhayati, Yusof

    Published 2016
    “…This automatic model selection algorithm is better than non-algorithm procedure which requires knowledge and extra time. …”
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    Thesis
  17. 17

    Modeling and Optimization of Tapered Rectangular Thin-walled Columns Subjected to Oblique Loading for Impact Energy Absorption by Siti Aishah, Rusdan, Tarlochan, Faris, Mohamad Rusydi, Mohamad Yasin

    Published 2013
    “…The approximation of the response then evaluated to check the fitness of the model to the true system. …”
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    Conference or Workshop Item
  18. 18
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    Ensemble Dual Recursive Learning Algorithms for Identifying Custom Tanks Flow with Leakage by Akib, Afifi, Saad , Nordin, Asirvadam , Vijanth Sagayan

    Published 2010
    “…Relative mass release of the leakage is introduced as the input for the simulation model and the data from the simulation model is taken at real time (on-line) to feed into the recursive algorithms. …”
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    Conference or Workshop Item
  20. 20

    Neural network based model predictive control for a steel pickling process by Kittisupakorn, P., Thitiyasook, P., Hussain, Mohd Azlan, Daosud, W.

    Published 2009
    “…The Levenberg-Marquardt algorithm is used to train the process models. In the control (MPC) algorithm, the feedforward neural network models are used to predict the state variables over a prediction horizon within the model predictive control algorithm for searching the optimal control actions via sequential quadratic programming. …”
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    Article