Search Results - (( java adaptation optimization algorithm ) OR ( parameter application means algorithm ))

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    A new hybrid deep neural networks (DNN) algorithm for Lorenz chaotic system parameter estimation in image encryption by Nurnajmin Qasrina Ann, Ayop Azmi

    Published 2023
    “…The research starts with developing the hybrid deep learning model consisting of DNN and a K-Means Clustering Algorithm. Then, the developed algorithm is implemented to estimate the parameters of the Lorenz system. …”
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    Slice sampler and metropolis hastings approaches for bayesian analysis of extreme data by Rostami, Mohammad

    Published 2016
    “…A simulation study shows that the slice sampler algorithm provides posterior means with low errors for the parameters along with a high level of stationarity in iteration series. …”
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    A novel quantum calculus-based complex least mean square algorithm (q-CLMS) by Sadiq, A., Naseem, I., Khan, S., Moinuddin, M., Togneri, R., Bennamoun, M.

    Published 2022
    “…The proposed algorithm is based on Wirtinger calculus and is called as q- Complex Least Mean Square (q-CLMS) algorithm. …”
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    A novel quantum calculus-based complex least mean square algorithm (q-CLMS) by Sadiq, A., Naseem, I., Khan, S., Moinuddin, M., Togneri, R., Bennamoun, M.

    Published 2022
    “…The proposed algorithm is based on Wirtinger calculus and is called as q- Complex Least Mean Square (q-CLMS) algorithm. …”
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    Article
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    A novel quantum calculus-based complex least mean square algorithm (q-CLMS) by Sadiq, A., Naseem, I., Khan, S., Moinuddin, M., Togneri, R., Bennamoun, M.

    Published 2023
    “…The proposed algorithm is based on Wirtinger calculus and is called as q- Complex Least Mean Square (q-CLMS) algorithm. …”
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    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
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    Effectiveness of nature-inspired algorithms using ANFIS for blade design optimization and wind turbine efficiency by Sarkar, Md. Rasel, Julai, Sabariah, Chong, Wen Tong, Toha @ Tohara, Siti Fauziah

    Published 2019
    “…The difference between optimized and predicted is very small which implies the effectiveness of nature-inspired algorithms in this application. In addition, the value of RMSE and R2 of the ABC-ANFIS algorithm were lower (indicating that the result obtained is more accurate) than the ACO and PSO algorithms…”
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    Integrated optimal control and parameter estimation algorithms for discrete-time nonlinear stochastic dynamical systems by Kek, Sie Long

    Published 2011
    “…In the first sub-algorithm, the state mean propagation removes the Gaussian white noise to obtain the expected solution. …”
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    Modeling and Analysis of New Hybrid Clustering Technique for Vehicular Ad Hoc Network by Abdulrazzak H.N., Hock G.C., Mohamed Radzi N.A., Tan N.M.L., Kwong C.F.

    Published 2023
    “…To address these problems, a novel method combining a covering rough set and a K-Means clustering algorithm (RK-Means) was proposed in this paper. …”
    Article
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    Enhanced artificial bee colony-least squares support vector machines algorithm for time series prediction by Zuriani, Mustaffa

    Published 2014
    “…Over the past decades, the Least Squares Support Vector Machines (LSSVM) has been widely utilized in prediction task of various application domains. Nevertheless, existing literature showed that the capability of LSSVM is highly dependent on the value of its hyper-parameters, namely regularization parameter and kernel parameter, where this would greatly affect the generalization of LSSVM in prediction task. …”
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    Removal of heavy metals from water by functionalized carbon nanotubes with deep eutectic solvents: An artificial neural network approach / Seef Saadi Fiyadh by Seef Saadi , Fiyadh

    Published 2019
    “…Moreover, various indicators were implemented to evaluate the ANN model’s productivity including relative root mean square error (RRMSE), mean square error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE) and relative error (RE). …”
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