Search Results - (( variable optimization based algorithm ) OR ( parameter estimation study algorithm ))

A decomposed streamflow non-gradientbased artificial intelligence forecasting algorithm with factoring in aleatoric and epistemic variables / Wei Yaxing by Wei , Yaxing

“…Firefly algorithm outperformed the other metaheuristic algorithms used to solve this proposed hybrid artificial intelligence model regarding parameter sensitivity. …”
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Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in �oruh River by Katipo?lu O.M., Kartal V., Pande C.B.

“…The hybrid model is a novel approach for estimating sediment load based on various input variables. …”

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.

“…This study proposed an improved parameter estimation procedure for PEMFCs by using the GOOSE algorithm, which was inspired by the adaptive behaviours found in geese during their relaxing and foraging times. …”

Feature selection and optimal neural network algorithm for the state of charge estimation of lithium-ion battery for electric vehicle application by Hossain Lipu M.S., Hannan M.A., Hussain A.

Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm by Reza M.S., Hannan M.A., Mansor M., Ker P.J., Rahman S.A., Jang G., Mahlia T.M.I.

“…In addition, to validate the prediction performance of the proposed LSA + LSTM model, extensive comparisons are performed with other popular optimization-based deep learning methods including artificial bee colony (ABC) based LSTM (ABC + LSTM), gravitational search algorithm (GSA) based LSTM (GSA + LSTM), and particle swarm optimization (PSO) based LSTM (PSO + LSTM) model using different error matrices. …”

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. …”
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Determination of tree stem volume : A case study of Cinnamomum by Noraini Abdullah

“…Illustrations and algorithms are incorporated into the procedures. Non-normal and nonlinear data variables are addressed, hence data characterization is presented. …”
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Optimization of Lipase Catalysed Synthesis of Sugar Alcohol Esters Using Taguchi Method and Neural Network Analysis by Adnani, Seyedeh Atena

“…Subsequent quantitative studies on main effects of parameters governing the reactions based on conversion of fatty acid were conducted by titration analysis. …”
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Information Theoretic-based Feature Selection for Machine Learning by Muhammad Aliyu, Sulaiman

“…Three major factors that determine the performance of a machine learning are the choice of a representative set of features, choosing a suitable machine learning algorithm and the right selection of the training parameters for a specified machine learning algorithm. …”
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Modeling of cupping suction system based on system identification method by Kavindran, Suresh

“…Cupping suction plant identification utilizing a nonlinear model based on the modified Sine Cosine Algorithm (mSCA). …”
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Groundwater quality assessment and optimization of monitored wells using multivariate geostatistical techniques in Amol-Babol Plain, Iran by Narany, Tahoora Sheikhy

“…A new optimization approach was proposed for redesign monitoring network wells using optimization algorithm based on the vulnerability of aquifer to contaminations, estimation error of sampling wells, nearest distance between wells, and source of contamination in the study area. …”
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Optimization of Microbial Electrolysis Cell for Sago Mill Wastewater Derived Biohydrogen via Modeling and Artificial Neural Network by Mohamad Afiq, Mohd Asrul

“…Model validity describes the first sub-objective, which is to solve the complexity of the nonlinear interaction of multiple MEC input variables related to the hydrogen production rate response using artificial neural networks (ANN) before validating the mathematical modeling results by comparing experimental data with the predicted substrate concentration profile and hydrogen production rate profile based on the re-estimated input values of the model parameters using single-objective optimization based on the nonlinear convex method using gradient descent algorithm. …”
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Identification of debris flow initiation zones using topographic model and airborne laser scanning data by Lay, Usman Salihu, Pradhan, Biswajeet

“…Conditioning parameters were numerically optimized to identify the arbitrarily maximum model basis function for eleven variables, using MARSplines analysis (algorithm). …”
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Proving the efficiency of alternative linear regression model based on mean square error (MSE) and average width using aquaculture data by Awang Nawi, Mohamad Arif, Wan Ahmad, Wan Muhamad Amir, Mohd Ibrahim, Mohamad Shafiq, Mamat, Mustafa, Khamis, Mohd Fahdli, Mohamed, Mohamad Afendee

“…Typically, the alternative method optimized by minimizing the mean square error (MSE) and average width. The results of the study showed a positive improvement for the estimation of parameters generated through these alternative methods. © BEIESP.…”
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Development of an artificial neural network algorithm for predicting the cutting force in end milling of Inconel 718 alloy by Hossain, Ishtiaq, Amin, A. K. M. Nurul, Patwari, Muhammed Anayet Ullah

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Proving the eficiency of Alternative Linear regression Model Based on Mean Square Error (MSE) and average width using aquaculture data by Awang Nawi, Mohamad Arif, Wan Ahmad, Wan Muhamad Amir, Mohd Ibrahim, Mohamad Shafiq, Mamat, Mustafa, Khamis, Mohd Fadhli, Mohamed, Mohamad Afendee

“…Typically, the alternative method optimized by minimizing the mean square error (MSE) and average width. The results of the study showed a positive improvement for the estimation of parameters generated through these alternative methods.…”
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Multi-objective optimization of process variables for MWCNT-added electro-discharge machining of 316L steel by Al-Amin, M., Abdul-Rani, A.M., Ahmed, R., Shahid, M.U., Zohura, F.T., Rani, M.D.B.A.

“…The best 21 solution sets predicted through the multi-objective optimization tool called non-dominated sorting genetic algorithm-II (NSGA-II) obeying the set objective functions are proposed which are obtained from the Pareto optimal frontiers. …”
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Multi-objective optimization of process variables for MWCNT-added electro-discharge machining of 316L steel by Al-Amin, M., Abdul-Rani, A.M., Ahmed, R., Shahid, M.U., Zohura, F.T., Rani, M.D.B.A.

“…The best 21 solution sets predicted through the multi-objective optimization tool called non-dominated sorting genetic algorithm-II (NSGA-II) obeying the set objective functions are proposed which are obtained from the Pareto optimal frontiers. …”
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Comparison between specifications of linear regression and spatial-temporal autoregressive models in mass appraisal valuation for single storey residential property by Jahanshiri, Ebrahim

“…Furthermore, various spatial, temporal and spatio-temporal neighbourhood and weighting schemes, optimization algorithms and lag and error modelling scenarios were created and tested with the data. …”
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Grid-based remotely sensed hydrodynamic surface runoff model using emissivity coefficient / Jurina Jaafar by Jaafar, Jurina

“…The development of the model strongly depends on the physical based parameters, examples of physical parameters that include roughness Manning’s n, hydraulic conductivity, soil depth, river geometry and the surface land cover. …”
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