Search Results - (( data optimization method algorithm ) OR ( parametric simulation model algorithm ))

Quality of service in mobile IP networks with parametric multi-channel routing algorithms based on linear programming approach by Gholizadeh, Somayyeh

“…Furthermore, Optimized Parametric Topology Control Routing algorithm performs significantly better than Triangular Routing Method and Change Foreign Agent Algorithm. …”
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Analysis of multiexponential transient signals using interpolation-based deconvolution and parametric modeling techniques by Salami, Momoh Jimoh Eyiomika, Ismail, Z.

“…One method of overcoming this d1ficulty is by incorporating the spline interpolation algorithm into the nonlinear preprocessing procedure. …”
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Physics-guided deep neural network to characterize non-Newtonian fluid flow for optimal use of energy resources by Kumar, A., Ridha, S., Narahari, M., Ilyas, S.U.

“…The proposed DNN algorithm is structured to incorporate initial/boundary conditions in cylindrical coordinates and approximate the solution without the aid of any simulated or training data. …”
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Active vibration control of a flexible beam structure using chaotic fractal search algorithm by Tuan Abdul Rahman, Tuan Ahmad Zahidi, As'arry, Azizan, Abdul Jalil, Nawal Aswan

“…Parametric modeling of the system was developed using auto-regressive exogenous (ARX) model structure based on the input-output data from previous experimental finding. …”
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Parametric analysis of critical buckling in composite laminate structures under mechanical and thermal loads: a finite element and machine learning approach by Shabbir Ahmed, Omar, Syed Mohamed Ali, Jaffar, Aabid, Abdul, Hrairi, Meftah, Mohd Yatim, Norfazrina Hayati

“…Later, various structural and material parameters like spacing ratio, opening ratio, hole shape, fiber orientation, and laminate sequence are systematically varied. Subsequently, simulation data from numerous cases are utilized to identify the best parameter combination using machine learning algorithms. …”
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Power prediction using the wind turbine power curve and data-driven approaches / Ehsan Taslimi Renani by Ehsan Taslimi , Renani

“…In order to demonstrate the efficiency of the proposed method, it is compared rigorously with several parametric and nonparametric models. …”
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Support vector machine and its applications for linear and nonlinear regression in the presence of outliers of high dimensional data by Sleabi, Waleed Dhhan

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A simulation study of a parametric mixture model of three different distributions to analyze heterogeneous survival data by Mohammed, Yusuf Abbakar, Yatim, Bidin, Ismail, Suzilah

“…In this paper a simulation study of a parametric mixture model of three different distributions is considered to model heterogeneous survival data.Some properties of the proposed parametric mixture of Exponential, Gamma and Weibull are investigated.The Expectation Maximization Algorithm (EM) is implemented to estimate the maximum likelihood estimators of three different postulated parametric mixture model parameters.The simulations are performed by simulating data sampled from a population of three component parametric mixture of three different distributions, and the simulations are repeated 10, 30, 50, 100 and 500 times to investigate the consistency and stability of the EM scheme.The EM Algorithm scheme developed is able to estimate the parameters of the mixture which are very close to the parameters of the postulated model.The repetitions of the simulation give parameters closer and closer to the postulated models, as the number of repetitions increases, with relatively small standard errors.…”
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An intelligent framework for modelling and active vibration control of flexible structures by Mohd. Hashim, Siti Zaiton

“…Parametric and non-parametric modelling of such systems is investigated. …”
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A parametric mixture model of three different distributions: An approach to analyse heterogeneous survival data by Mohammed, Yusuf Abbakar, Yatim, Bidin, Ismail, Suzilah

“…A parametric mixture model of three different distributions is proposed to analyse heterogeneous survival data.The maximum likelihood estimators of the postulated parametric mixture model are estimated by applying an Expectation Maximization Algorithm (EM) scheme.The simulations are performed by generating data, sampled from a population of three component parametric mixture of three different distributions. …”
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Identification algorithms of flexible structure using neural networks by Ismail, R., Ismail, A. Y., Mat Darus, I. Z.

“…The least square and recursive least square are used to obtain linear parametric model of the system. Furthermore, non-parametric models of the system are developed using Multi-layer Perceptron Neural Networks (MLP-NN) and Elman Neural Networks (ENN). …”
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Identification algorithms of flexible structure using neural networks by Ismail, Razali, Ismail, A.Y, Mat Darus, I.Z.

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Modelling and optimisation of blood glucose control for type 1 diabetes using multi-parametric programming and model-based predictive control (mp-MPC) / Associate Professor Dr Ayub... by Md. Som, Ayub

“…In doing so, Multi-Parametric Programming technique is used to develop the computer algorithm; whereas Model-Based Predictive Control (MPC) is adopted for the design of the controller. …”
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Combustion modelling of an industrial municipal waste combustor in Malaysia by Hussain, Ahmad, Ani, Farid Nasir, Sulaiman, Norzalia, Adnan, Mohammed Fadzil

“…CFD flow simulations can already permit detailed parametric variations of design variables. …”
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Parametric modelling of a TRMS using dynamic spread factor particle swarm optimisation by Toha, Siti Fauziah, Abd Latiff, I., Mohamad, M., Tokhi, M Osman

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Parametric system identification and active vibration control of vibrational structures using genetic algorithm by Mohammad Sofi @ Aziz, Azfi Zaihan

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Non-Parametric and Parametric Estimations of Cure Fraction Using Right-and Interval-Censored Data by Aljawdi, Bader

“…In this thesis, we considered two methods via the expectation maximization (EM) algorithm for cure rate estimation based on the BCH model using the two censoring types common to cancer clinical trials; namely, right and interval censoring. …”
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Parametric Cox’s Model for partly interval-censored data with application to AIDS studies by Elfaki, Faiz Ahmed Mohamed, Azram, Mohammad, Usman, Mustofa

“…The Parametric Cox’s Proportional Hazard Model based on Expectation-Maximization (EM) algorithm for partly interval-censored data is studied. …”
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GEE-smoothing spline in semiparametric model with correlated nominal data by Ibrahim, Noor Akma, Suliadi

“…In this paper we propose GEE‐Smoothing spline in the estimation of semiparametric models with correlated nominal data. The method can be seen as an extension of parametric generalized estimating equation to semiparametric models. …”
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Semiparametric binary model for clustered survival data by Arlin, Rifina, Ibrahim, Noor Akma, Arasan, Jayanthi, Abu Bakar, Mohd Rizam

“…A backfitting algorithm is used in the derivation of the estimating equation for the parametric and nonparametric components of a semiparametric binary covariate model. …”
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