Search Results - (( using simulation using algorithm ) OR ( variable training based algorithm ))
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1
A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models
Published 2017“…The results show that the neural network optimized with genetic algorithm and trained with an optimally and intelligently selected input vector containing historical load and meteorological variables produced the best prediction accuracy. © 2017, UK Simulation Society. …”
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Article -
2
Design optimization of valve timing at various engine speeds using Multi-Objective Genetic Algorithm (MOGA)
Published 2008“…Majority of the research works in this project were in CAE software environment and method to implement optimization to 1D engine simulation. The paper conducts design optimization of CAMPRO 1.6L (S4PH) engine valve timing at various engine speeds using multi-objective genetic algorithm (MOGA) for the future variable valve timing (VVT) system research and development. …”
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Proceeding Paper -
3
Neural network based model predictive control for a steel pickling process
Published 2009“…The Levenberg-Marquardt algorithm is used to train the process models. In the control (MPC) algorithm, the feedforward neural network models are used to predict the state variables over a prediction horizon within the model predictive control algorithm for searching the optimal control actions via sequential quadratic programming. …”
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4
Effects of user selected conditions on modeling of dynamic systems using adaptive fuzzy model
Published 2001“…The implementation and the computational aspects of the training algorithm are also highlighted. Three examples of discrete-time nonlinear systems are used in the simulation study to show the effects of user selected conditions on the identification process. …”
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5
Optimal Weighted Learning of PCA and PLS for Multicollinearity Discriminators and Imbalanced Groups in Big Data (S/O: 13224)
“…This study developed an algorithm for statistical classification that enable ones to classify a future data to one of predetermined groups based on the measured data which facing two major threats; (i) multicollinearity among the measured variables and (ii) imbalanced groups. …”
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Monograph -
6
A parallel genetic algorithm-based TSK-Fuzzy system for dynamic car-following modeling
Published 2009“…This paper presents the application of Parallel Genetic Algorithm (PGA)-based Takagi Sugeno Kang (TSK)-Fuzzy approach for dynamic car-following modeling in the traffic simulation software. …”
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7
Design of artificial intelligence based speed estimator for DC drives / Pauziah Saleh
Published 2006“…The data from the closed loop DC motor with PID controller is used. The variable input data of armature voltage, armature current and output speed were collected by using simulation of the system. …”
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Thesis -
8
Reinforcement learning-based target tracking for unmanned aerial vehicle with achievement rewarding and multistage traning
Published 2022“…Third, the concept of multistage training based on the dynamic variables was proposed as an opposing concept to one-stage combinatory training. …”
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9
Design of Artificial Neural Network (ANN) based rotor speed estimator for DC drives / Siti Mutrikah Abd Mokhsin
Published 2002“…For this purpose the Levenberg-Marquardt back-propagation algorithm was used. The training took only a few minutes on a PC and for this purpose 30000 inputoutput training data were used. …”
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Machine Learning Based Optimal Design of On-Road Charging Lane for Smart Cities Applications
Published 2025“…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. …”
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Neural network based adaptive pid controller for shell-and-tube heat exchanger
Published 2019“…Dynamic time series neural network model was used together with Levenberg-Marquardt algorithm as the training method. …”
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Student Project -
13
Neural network based adaptive pid controller for shell-and-tube heat exchanger: article
Published 2019“…Dynamic time series neural network model was used together with Levenberg-Marquardt algorithm as the training method. …”
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14
Predicting crop yield and field energy output for oil palm using genetic algorithm and neural network models
Published 2019“…Finally, this research concluded that a genetic algorithm is useful for selecting input variables in oil palm production. …”
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15
Information Theoretic-based Feature Selection for Machine Learning
Published 2018“…The second test evaluates IFS in a controlled study using simulated datasets. Moreover, the third test used ten natural domain datasets obtained from UCI Repository, in about fifteen different experiments, using three to four different Machine Learning Algorithms for performance evaluation. …”
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16
Permodelan Rangkaian Neural Buatan Untuk Penilaian Kendiri Teknologi Maklumat Guru Pelatih
Published 2001“…The data was collected from various field of studies among teacher trainees in teacher training institutes. Data containing eleven predictive variables was used to train and test neural network model. …”
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17
Rank-based optimal neural network architecture for dissolved oxygen prediction in a 200L bioreactor
Published 2017“…Thus it is beneficial to model the relationship of DO concentration with these variables based on real process data for further use in controller design. …”
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Conference or Workshop Item -
18
Sensorless induction motor speed control for electric vehicles using enhanced hybrid flux estimator with ann-ifoc controller
Published 2022“…The sensorless ANN-IFOC was modelled, simulated, and tested using MATLAB/Simulink for a 20Hp EV motor based on a small Renault Twizy EV model and triggered by the space-vector pulse-width modulation (SVPWM). …”
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19
Artificial neural network for anomalies detection in distillation column
Published 2017“…The network is trained using back propagation algorithm to determine root mean square error (RMSE). …”
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Article -
20
Breast Cancer Diagnosis Using Neuro-CBR Approach
Published 2005“…In this study, the Neural Network (NN) simulator with backpropagation algorithm was developed to predict the condition of the breast cancer tumor whether it is benign or maligant and Case-Base Reasoning (CBR) engine developed to classify the cancer stages as well as suggesting appropriate treatment to the patient. …”
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