Search Results - (( data equations using algorithm ) OR ( parameter optimization based algorithm ))

Modeling and multi-objective optimal sizing of standalone photovoltaic system based on evolutionary algorithms by Ridha, Hussein Mohammed

“…The IEM algorithm uses the attraction-repulsion mechanism to change the positions of solutions towards the optimality. …”
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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 fitness function used is the correlation function in the SKF algorithm to optimize the cipher image produced using the Lorenz system. …”
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Fuzzy clustering for genetic algorithm based optimized ellipse data in classifying face emotion by Muthukaruppan, Karthigayan, Mohd Rizon, Mohamed Juhari, Sazali, Yaacob, Ramachandran, Nagarajan, Masanori, Sugisaka, Mohd Rozailan, Mamat

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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.

“…In this research, a novel algorithm (Herschel Bulkley Network) is introduced to simulate the non-Newtonian fluid flow in a pipe using data redundant deep neural network (DNN) for fully developed, laminar, and incompressible flow conditions. …”
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Modelling and control of heat exchanger by using bio-inspired algorithm by Daud, Nur Atiqah

“…In this study, data from heat exchanger experiment was used to determine the parameter of ARMAX equation and by using GA and PSO, all the parameters were optimized. …”
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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 of the most promising approaches is based on optimal inverse Xltering followed by fitting an autoregressive moving average ( A M ) model to the deconvolved data so that its AR parameters are determined by solving high order Yule- Walker equations (HOYWE) via the singular value decomposition (SVD) algorithm. …”
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Identifying and estimating solar cell parameters using an enhanced slime mould algorithm by Logeswaary, Devarajah, Mohd Ashraf, Ahmad, Jui, Julakha Jahan

“…In general, ESMA outperformed the original SMA and other recent algorithms. Also, in order to provide a close approximation of the empirical I-V data of the real PV modules and cells, ESMA was able to determine the optimal parameter values for photovoltaic models.…”
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Kinetic parameters estimation of the Escherichia coli (E. coli) model by Garra Rufa-inspired Optimization Algorithm (GRO) by Jasni, Mohamad Zain, Azrag, Mohammed Adam Kunna, Saiful Farik, Mat Yatin, Aldehim, Ghadah, Zuhaira, Muhammad Zain, Shaiba, Hadil, Alturki, Nazik, Sapiah, Sakri, Azlinah, Mohamed, Jaber, Aqeel S.

“…In this study, experimental data was used to estimate seven kinetic parameters. …”
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Modeling And Optimization Of Physical Vapour Deposition Coating Process Parameters For Tin Grain Size Using Combined Genetic Algorithms With Response Surface Methodology by Mohamad Jaya, Abdul Syukor, Muhamad, Mohd Razali, Abd Rahman, Md Nizam, Mohammad Jarrah, Mu'ath Ibrahim, Hasan Basari, Abd Samad

“…Additionally,analysis of variance(ANOVA) was used to determine the significant factors influencing resultant TiN coating grain size.Based on that,a quadratic polynomial model equation was developed to represent the process variables and coating grain size.Then,in order to optimize the coating process parameters, genetic algorithms (GAs) were combined with the RSM quadratic model and used for optimization work.Finally,the models were validated using actual testing data to measure model performances in terms of residual error and prediction interval (PI).The result indicated that for RSM,the actual coating grain size of validation runs data fell within the 95% (PI) and the residual errors were less than 10 nm with very low values, the prediction accuracy of the model is 96.09%.In terms of optimization and reduction the experimental data,GAs could get the best lowest value for grain size then RSM with reduction ratio of ≈6%, ≈5%, respectively.…”
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Parameter estimation of stochastic differential equation by Haliza Abd. Rahman, Arifah Bahar, Norhayati Rosli, Madihah Md. Salleh

“…The use is illustrated using observed data of opening share prices of Petronas Gas Bhd. …”
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Assisted History Matching by Using Genetic Algorithm and Discrete Cosine Transform by Abdul Rashid, Abdul Hadi

“…To achieve the objective stated above, a conceptual reservoir model was built based on a set of average reservoir data. Next, fluid flow equations were derived to obtain the forward model and eventually, the objective function. …”
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PSO and Linear LS for parameter estimation of NARMAX/NARMA/NARX models for non-linear data / Siti Muniroh Abdullah by Abdullah, Siti Muniroh

“…Typically, parameter estimation is performed using various types of Least Squares (LS) algorithms due to its stable and efficient numerical computation. …”
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HYBRID SINE COSINE AND FITNESS DEPENDENT OPTIMIZER FOR INCOMPLETE DATASET by Chiu, Po Chan, Ali, Selamat, Kuok, King Kuok

“…The hybrid sine cosine and fitness dependent optimizer (SC-FDO) introduces four modifications to the original fitness dependent optimizer (FDO) algorithm to improve its exploit-explore tradeoff with a faster convergence speed. …”
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Base drag estimation in suddenly expanded supersonic flows using backpropagation genetic and recurrent neural networks by Jaimon, Dennis Quadros, Prashanth, T., Khan, Sher Afghan

“…A batch mode of training was employed to conduct a parametric study for adjusting and optimizing the neural network parameters. Due to the requirement of massive data for batch mode training, the data required for training was achieved using the response equations developed through response surface methodology. …”
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Parameter estimation of multicomponent transient signals using deconvolution and ARMA modelling techniques by Salami, Momoh Jimoh Emiyoka, Sidek, Shahrul Na'im

“…Using an autoregressive moving (ARMA) model whose AR parameters are determined by solving high-order Yule-Walker equations (HOYWE) via the singular value decomposition (SVD) algorithm can alleviate this shortcoming. …”
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3D prediction of tunneling-induced ground movements based on a hybrid ANN and empirical methods by Hajihassani, M., Kalatehjari, R., Marto, A., Mohamad, H., Khosrotash, M.

“…To overcome these problems, the use of optimization algorithms to train ANNs is of advantage. …”
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3D prediction of tunneling-induced ground movements based on a hybrid ANN and empirical methods by Hajihassani, M., Kalatehjari, R., Marto, A., Mohamad, H., Khosrotash, M.

“…To overcome these problems, the use of optimization algorithms to train ANNs is of advantage. …”
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Analysis of parallel flow type internally cooled membrane-based liquid desiccant dehumidifier using a neural networks approach by Quadros, Jaimon Dennis, Khan, Sher Afghan, T., Prashanth

“…Backpropagation algorithm (BP), artificial bee colony (ABC), and genetic algorithm (GA) models were used to train the neural network (NN) parameters using the data collected from the CCD-based response equation. …”
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Design and implementation of a real-time adaptive learning algorithm controller for a 3-DOF parallel manipulator / Mustafa Jabbar Hayawi by Hayawi, Mustafa Jabbar

“…The parallel manipulator is optimized based on the performance indices to obtain on the optimal design parameters for achieved maximum performance of the parallel manipulator. …”
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Enhancing reservoir simulation models with genetic algorithm optimized neural networks across diverse climatic zones / Saad Mawlood Saab by Saad Mawlood , Saab

“…The research improved the predictive models by integrating them with the Genetic Algorithm (GA). The optimizer algorithm (i.e., GA) determines the optimal input variables and internal parameters in the prediction models. …”
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