Search Results - (( panel optimization method algorithm ) OR ( parameter estimation learning algorithm ))
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1
Using the evolutionary mating algorithm for optimizing deep learning parameters for battery state of charge estimation of electric vehicle
Published 2023“…This paper presents the application of a recent metaheuristic algorithm namely Evolutionary Mating Algorithm (EMA) for optimizing the Deep Learning (DL) parameters to estimate the state of charge (SOC) of a battery for an electric vehicle in the real environment. …”
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2
Parameter Estimation of Lorenz Attractor: A Combined Deep Neural Network and K-Means Clustering Approach
Published 2022“…Therefore, it is crucial to assess the parameter of chaotic systems. To solve the issue of parameter estimation for a chaotic system, deep learning is utilized. …”
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Conference or Workshop Item -
3
An Improved Scatter Search Algorithm for Parameter Estimation in Large-Scale Kinetic Models of Biochemical Systems
Published 2019“…Methods: This paper proposes an improved scatter search algorithm to address the challenging parameter estimation problem. …”
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4
An improved scatter search algorithm for parameter estimation in large-scale kinetic models of biochemical systems
Published 2019“…Methods: This paper proposes an improved scatter search algorithm to address the challenging parameter estimation problem. …”
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5
Optimization of stiffened panel fatigue life by using finite element analysis
Published 2020“…Three optimization methods: screening, multi-objective genetic algorithm and adaptive multi-objective algorithm were adopted in this study. …”
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Thesis -
6
Hybrid MPPT algorithm for mismatch photovoltaic panel application / Muhammad Iqbal Mohd Zakki
Published 2019“…MATLAB/Simulink is used to develop, test, optimize, and verify the proposed hybrid MPPT algorithm, and PSO-PID tuning method, before embedded in the MPPT system for the hardware implementation as a research product. …”
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7
Comparison of machine learning algorithms for estimating mangrove age using sentinel 2A at Pulau Tuba, Kedah, Malaysia / Fareena Faris Francis Singaram
Published 2021“…The parameters involved to estimate the mangrove age are differences feature selection and different supervised machine learning algorithm. …”
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8
A Bayesian parameter learning procedure for nonlinear dynamical systems via the ensemble Kalman filter
Published 2018“…Within the parameter learning steps, the MCMC algorithm requires to perform state estimation for which the target distribution is constructed by using the Ensemble Kalman filter (EnKF). …”
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9
A new hybrid deep neural networks (DNN) algorithm for Lorenz chaotic system parameter estimation in image encryption
Published 2023“…Then, the developed algorithm is implemented to estimate the parameters of the Lorenz system. …”
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10
Optimization variation for multiple heuristic approaches in solar tracking
Published 2023Subjects: “…Genetic algorithm…”
Conference paper -
11
Performance analysis of PSO MPPT for photovoltaic (PV) system during irradiance changes / Kharismi Burhanudin
Published 2018“…Particle swarm optimization is soft computing methods which follow the bird swarm to track maximum power from PV panel. …”
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12
Parameter estimation in computational systems biology models: a comparative study of initialization methods in global optimization
Published 2022“…Estimating parameters values is difficult and time-consuming process due to their highly nonlinear and huge number of kinetic parameters involved. …”
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13
RECURSIVE LEARNING ALGORITHMS ON RBF NETWORKS FOR NONLINEAR SYSTEM IDENTIFICATION
Published 2010“…The thesis also try to investigate the influence of initialization of RBF weights parameters on the overall learning performance using random method and advanced unsupervised learning, such as clustering techniques, as a comparison. …”
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14
Comparative analysis of three approaches of antecedent part generation for an IT2 TSK FLS
Published 2017“…Since extreme learning machine is a non-iterative estimation procedure, it is faster than gradient-based algorithms which are iterative. …”
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15
Neural network algorithm development for Ion Sensitive Field Effect Transistor (ISFET) sensor / Muhammad Nasrul Hakim Adenan
Published 2013“…The ANN model performance can be optimized by altering certain parameters in the learning algorithm. The results show that the model is able to predict with 97% accuracy and has strong and precise estimation ability with R-factor of 91.55%.…”
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16
Neural network algorithm development for Ion Sensitive Field Effect Transistor (ISFET) sensor: article / Muhammad Nasrul Hakim Adenan and Maizatul Zolkapli
Published 2013“…The ANN model performance can be optimized by altering certain parameters in the learning algorithm. The results show that the model is able to predict with 97% accuracy and has strong and precise estimation ability with R-factor of 91.55%.…”
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17
Real-Time State of Charge Estimation of Lithium-Ion Batteries Using Optimized Random Forest Regression Algorithm
Published 2024“…This paper presents an improved machine learning approach for the accurate and robust state of charge (SOC) in electric vehicle (EV) batteries using differential search optimized random forest regression (RFR) algorithm. …”
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Lithium-ion Battery State of Charge Estimation Method Using Optimized Deep Recurrent Neural Network Algorithm
Published 2023Conference Paper -
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Performance analyses of various photovoltaic power plant based on local spectral irradiances in Malaysia using genetic algorithm
Published 2023“…Besides, the genetic algorithm has showed that it is an effective and efficient way to perform estimation of optimal tilt angle and orientation angle for solar sites with genetic algorithm as it is able to produce correct and accurate result in a short amount of time.…”
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Final Year Project / Dissertation / Thesis -
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Optimization of a reliable grid-connected PV-based power plant
Published 2023“…Maximum power point tracking (MPPT) has been used in this project in optimizing the PV system. One of the methods of the MPPT technique which is Perturb and Observe (P&O) algorithm, easy to implement and has efficient performance in solving the optimization problem. …”
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Proceeding Paper
