Search Results - (( data estimation method algorithm ) OR ( variable training based algorithm ))*

Design of artificial intelligence based speed estimator for DC drives / Pauziah Saleh by Saleh, Pauziah

“…This thesis describes the design of Artificial Intelligence Based speed estimator for separately excited DC motor using feedforward backpropagation method. …”
Get full text
Get full text

Robust Data Fusion Techniques Integrated Machine Learning Models For Estimating Reference Evapotranspiration by Chia, Min Yan

“…The Bayesian model averaging (BMA) enhanced the estimation of the ensembles of the base MLP, SVM and ANFIS. …”
Get full text
Get full text

Air Pollution Index Estimation Model Based On Artificial Neural Network by Mohammed Nasser, Al-Subaie

“…Environmental conservation efforts are always dealing with a complex problem because it involves a large number of variables. However, choosing a correct model structure, and optimum training algorithm with minimum complexity is crucial. …”
Get full text
Get full text

Applications of Data-driven Models for Daily Discharge Estimation Based on Different Input Combinations by Kumar M., Elbeltagi A., Pande C.B., Ahmed A.N., Chow M.F., Pham Q.B., Kumari A., Kumar D.

“…Decision trees; Errors; Flood control; Floods; Mean square error; Statistical tests; Agriculture management; Burhabalang river; Daily discharge; Data-driven model; Discharge estimation; Flood management; Training and testing; Water flood; Water industries; Water resources management; Rivers; algorithm; error analysis; estimation method; flood control; modeling; river discharge; river flow; India…”

Machine Learning Based Optimal Design of On-Road Charging Lane for Smart Cities Applications by Shanmugam Y., Narayanamoorthi R., Ramachandaramurthy V.K., Bernat P., Shrestha N., Son J., Williamson S.S.

“…This article proposes an effective machine learning (ML) approach to achieve the optimal design of the charging track, considering the cross-coupling effect. The algorithm not only aids in estimating the infrastructure cost of the charging lane but also predicts optimal design parameters using trained data. …”

Modelling monthly pan evaporation utilising Random Forest and deep learning algorithms by Abed M., Imteaz M.A., Ahmed A.N., Huang Y.F.

Real time self-calibration algorithm of pressure sensor for robotic hand glove system by Almassri, Ahmed M. M.

“…The model was tested using an untrained input data set in order to verify the Proposed model’s capability for implementing a self-calibration algorithm. …”
Get full text
Get full text

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. …”

Neural network based adaptive pid controller for shell-and-tube heat exchanger by Othman, Mohamad Hakimi

“…The dynamic behavior of the process is accurately modeled using nonlinear ARX model with 96.17% of validation accuracy and 97.5% of fit to estimation accuracy. Dynamic time series neural network model was used together with Levenberg-Marquardt algorithm as the training method. …”
Get full text
Get full text

Neural network based adaptive pid controller for shell-and-tube heat exchanger: article by Othman, Mohamad Hakimi

“…The dynamic behavior of the process is accurately modeled using nonlinear ARX model with 96.17% of validation accuracy and 97.5% of fit to estimation accuracy. Dynamic time series neural network model was used together with Levenberg-Marquardt algorithm as the training method. …”
Get full text
Get full text

Support vector machine and its applications for linear and nonlinear regression in the presence of outliers of high dimensional data by Sleabi, Waleed Dhhan

Get full text
Get full text
Get full text

Optimization of Lipase Catalysed Synthesis of Sugar Alcohol Esters Using Taguchi Method and Neural Network Analysis by Adnani, Seyedeh Atena

“…In addition comparison of statistical measures and performances between Taguchi method and ANN shows that ANN was slightly better than Taguchi for data fitting and estimation capabilities. …”
Get full text
Get full text

A comparative study on aviation arrival delay prediction using machine learning methods by Chew, Pui Ting

“…The F1 score for ANN train set managed to achieve 0.9 and maintain at 0.81 for its test set despite having outlier data in 2020. …”
Get full text

Improvement of an integrated global positioning system and inertial navigation system for land navigation application by Hasan, Ahmed Mudheher

“…This work also presents a new method for de-noising the GPS and INS data and estimate the INS error using wavelet multi-resolution analysis algorithm (WMRA) based particle swarm optimization (PSO) with a well designed structure appropriate for practical and real time implementations due to its very short optimizing time and elevated accuracy. …”
Get full text
Get full text

Production and characterization of biochar derived from oil palm wastes, and optimization for zinc adsorption by Zamani, Seyed Ali

“…The input and output of the RSM design was used in artificial neural networks for training purpose. The incremental back propagation algorithm demonstrated the best results and which has been used as learning algorithm for ANN in combination with Genetic Algorithm in the optimization. …”
Get full text
Get full text

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. …”
Get full text
Get full text
Get full text

Semiparametric inference procedure for the accelarated failure time model with interval-censored data by Karimi, Mostafa

“…The rank-based methods, estimating algorithms, and resampling techniques that are developed do not involve the difficulties of the existing estimating procedures. …”
Get full text
Get full text
Get full text

Detection and Classification of Moving Objects for an Automated Surveillance System by Md. Tomari, Mohd Razali

“…All the methods have been tested on video data and the experimental results have demonstrated a fast and robust system …”
Get full text
Get full text

Detection and classification of moving objects for an automated surveillance system by Md Tomari, Mohd Razali

“…All the methods have been tested on video data and the experimental results have demonstrated a fast and robust system…”
Get full text
Get full text

A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models by ul Islam, B., Baharudin, Z.

“…The focus of the paper is to propose a hybrid approach for the selection of the most influential input variables for the training and testing of neural network based hybrid models. …”
Get full text
Get full text