Search Results - (( parameter evaluation method algorithm ) OR ( square estimation means algorithm ))

Model selection approaches of water quality index data by Kamarudin, Nur Azulia, Ismail, Suzilah

“…In order to select the best model, it is vital to ensure that proper estimation method is chosen in the modelling process.Different estimators have been proposed for the estimation of parameters of a model, including the least square and iterative estimators.This study aims to evaluate the forecasting performances of two algorithms on water quality index (WQI) of a river in Malaysia based on root mean square error (RMSE) and geometric root mean square error (GRMSE).Feasible generalised least squares (FGLS) and iterative maximum likelihood (ML) estimation methods are used in the algorithms, respectively.The results showed that SUREMLE-Autometrics has surpassed SURE-Autometrics; another simultaneous selection procedure of multipleequation models.Two individual selections, namely Autometrics-SUREMLE and Autometrics-SURE, though showed consistency only for GRMSE.All in all, ML estimation is a more appropriate method to be employed in this seemingly unrelated regression equations (SURE) model selection.…”
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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.

“…The statistical analysis was used to evaluate the performance of the model based on the objective function which is the Mean Square Error (MSE). …”
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All-pass filtered x least mean square algorithm for narrowband active noise control by Mondal (Das), Kuheli, Das, Saurav, Abu, Aminudin, Hamada, Nozomu, Toh, Hoong Thiam, Das, Saikat, Faris, Waleed Fekry

“…Here first-order all pass filters with single parameter is used to improve the convergence of the LMS 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

“…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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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.

“…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. …”

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.

“…The statisti-cal analysis was used to evaluate the performance of the model based on the objective function which is the Mean Square Error (MSE). …”

Adaptive complex neuro-fuzzy inference system for non linear modeling and time series prediction by Shoorangiz, Mohammadreza

“…The first experiment is two dimensional Sinc function in which the proposed method with 4 rules has root mean square error of 0.017 while the root mean square error of adaptive network fuzzy inference system method is 0.074. …”
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Enhanced location and positioning in wimax networks with virtual mimo base station by Othman, Muhammad Hakim

“…Simulation results show that the proposed technique outperforms the linear least square (LLS) algorithm in terms of estimated location accuracy.…”
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Scene illumination classification based on histogram quartering of CIE-Y component by Hesamian, Mohammad Hesam

“…Those algorithms which performed estimation carrying out lots of calculation that leads in expensive methods in terms of computing resources. …”
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Data filtering of 5-axis inertial measurement unit using kalman filter by Nur Syazwani, Samsudin

“…The Kalman filter is a set of mathematical equations that provides an efficient computational (recursive) means to estimate the state of a process, in a way that minimizes the mean of the squared error. …”
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Enhancing riverine load prediction of anthropogenic pollutants: Harnessing the potential of feed-forward backpropagation (FFBP) artificial neural network (ANN) models by Khairudin K., Ul-Saufie A.Z., Senin S.F., Zainudin Z., Rashid A.M., Abu Bakar N.F., Anas Abd Wahid M.Z., Azha S.F., Abd-Wahab F., Wang L., Sahar F.N., Osman M.S.

“…Four widely used statistical performance assessment metrics were adopted to evaluate the performance of the various developed models: the root mean square error (RMSE), mean absolute error (MAE), mean relative error (MRE), and coefficient of determination (R2). …”

Battery remaining useful life estimation based on particle swarm optimization-neural network by Zuriani, Mustaffa, Mohd Herwan, Sulaiman

“…Concerning that matter, this study proposed hybrid Particle Swarm Optimization–Neural Network (PSO NN) for estimating battery RUL. In the evaluation of the proposed method, the effectiveness is assessed using the metrics of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). …”
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An inclusive multiple model for predicting total sediment transport rate in the presence of coastal vegetation cover based on optimized kernel extreme learning models by Jalil-Masir H., Fattahi R., Ghanbari-Adivi E., Asadi Aghbolaghi M., Ehteram M., Ahmed A.N., El-Shafie A.

Enhancing riverine load prediction of anthropogenic pollutants: harnessing the potential of feed-forward backpropagation (FFBP) artificial neural network (ANN) models by Khairudin, Khairunnisa, Ul-Saufie, Ahmad Zia, Senin, Syahrul Fithry, Zainudin, Zaki, Rashid, Ammar Mohd, Abu Bakar, Noor Fitrah, Anas Abd Wahid, Muhammad Zakwan, Azha, Syahida Farhan, Abd Wahab, Mohd Firdaus, Wang, Lei, Sahar, Farisha Nerina, Osman, Mohamed Syazwan

“…Four widely used statistical performance assessment metrics were adopted to evaluate the performance of the various developed models: the root mean square error (RMSE), mean absolute error (MAE), mean relative error (MRE), and coefficient of determination (R2). …”
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An In-depth Study of Ankle-Foot Orthosis Dynamics Modeling: Leveraging Non-Parametric Approach Via Artificial Neural Networks by Annisa, Jamali, Aida Suriana, Abdul Razak, Shahrol, Mohamaddan

“…Subsequently, the model structure was chosen, followed by parameter estimation through the selected algorithm. …”
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Performance evaluation of hybrid adaptive neuro-fuzzy inference system models for predicting monthly global solar radiation by Halabi, Laith M., Mekhilef, Saad, Hossain, Monowar

“…The proposed hybrid models include particle swarm optimization, genetic algorithm and differential evolution. To evaluate the capability and efficiency of the proposed models, several statistical indicators such as; root mean square error, co-efficient of determination and mean absolute bias error are used. …”
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Channel Modelling and Estimation in Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing Wireless Communication Systems by Hezam, Mohammed Abdo Saeed

“…The second issue addressed in this thesis is the channel estimation in MIMO OFDM systems. New time-domain (TD) adaptive estimation methods based on recursive least squares (RLS) and normalized least-mean squares (NLMS) algorithms are proposed. …”
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A comparative study of evolutionary algorithms and adapting control parameters for estimating the parameters of a single-diode photovoltaic module's model by Muhsen D.H., Ghazali A.B., Khatib T., Abed I.A.

“…Algorithms; Diodes; Errors; Iterative methods; Least squares approximations; Mean square error; Optimization; Parameter estimation; Parameter extraction; Photovoltaic cells; Coefficient of determination; Differential Evolution; Electromagnetism-like algorithm; Hybrid evolutionary algorithm; Photovoltaic; Photovoltaic modules; Root mean square errors; Single-diode models; Evolutionary algorithms; algorithm; comparative study; electromagnetic method; estimation method; experimental design; numerical method; parameterization; performance assessment; photovoltaic system…”

MULTIVARIABLE CLOSED-LOOP SYSTEM IDENTIFICATION USING ITERATIVE LEAKY LEAST MEAN SQUARES METHOD by MOHAMED OSMAN, MOHAMED ABDELRAHIM

“…In this research. novel algorithms have been developed to: (I) isolate the less interacting channe Is using a modified partial correlation algorithm. (2) achieve unbiased and consistent parameter estimates using an iterative LLMS algorithm and (3) develop parsimonious models for closed-loop MIMO systems. …”
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Reduced-rank technique for joint channel estimation in TD-SCDMA systems. by Ismail, Alyani, Sali, Aduwati, Mohd Ali, Borhanuddin, Khatun, Sabira

“…The adopted reduced rank technique is based on singular value decomposition algorithm. Equations for reduced rank-joint channel estimation (JCE) are derived and compared against traditional full rank-joint channel estimators: least square (LS) or Steiner, enhanced LS, and minimum mean square error algorithms. …”
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