Search Results - (( parameter optimization method algorithm ) OR ( linear validation using algorithm ))
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Optimization of milling parameters using ant colony optimization
Published 2008“…The result obtained from this simulation will compare with another method like Genetic Algorithm (GA) and Linear Programming Technique (LPT) to validation. …”
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Undergraduates Project Papers -
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PSO and Linear LS for parameter estimation of NARMAX/NARMA/NARX models for non-linear data / Siti Muniroh Abdullah
Published 2017“…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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Optimal power flow based on fuzzy linear programming and modified Jaya algorithms
Published 2017“…The proposed algorithms have been examined and validated using the IEEE 30-bus and IEEE 118-bus test systems. …”
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4
Estimation of photovoltaic models using an enhanced Henry gas solubility optimization algorithm with first-order adaptive damping Berndt-Hall-Hall-Hausman method
Published 2024“…Then in terms of methodology, the Enhanced Henry Gas Solubility Optimization (EHGSO) algorithm is combined with the Sine-Cosine mutualism phase of Symbiotic Organisms Search (SOS) for efficiently estimating the unknown parameters of PV models. …”
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Optimal power flow using a hybridization algorithm of arithmetic optimization and aquila optimizer
Published 2024“…In this paper, a hybridization method based on Arithmetic optimization algorithm (AOA) and Aquila optimizer (AO) solver namely, the AO-AOA is applied to solve the Optimal Power Flow (OPF) problem to independently optimize generation fuel cost, power loss, emission, voltage deviation, and L index. …”
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Neural network modeling and optimization for spray-drying coconut milk using genetic algorithm and particle swarm optimization
Published 2022“…The ANN model is further improved using GA and PSO. Each algorithm has its own parameters and is further optimized using RSM. …”
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Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm
Published 2025“…The orthogonal learning mechanism improves the performance of the original GOOSE algorithm. This FC model uses the root mean squared error as the objective function for optimizing the unknown parameters. …”
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Modeling flood occurences using soft computing technique in southern strip of Caspian Sea Watershed
Published 2012“…This approach was based on fuzzy expert system (FES) using Fuzzy Toolbox of MATLAB software. Genetic algorithm (GA) was employed to adjust parameters of FES and optimize the system. …”
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9
Efficiency improvement of a standalone photovoltaic system using fuzzy-based maximum power point tracking algorithm
Published 2016“…MPPT algorithms can be categorized into classical methods and artificial intelligence-based methods. …”
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10
Performance properties optimization of triaxial ceramic-palm oil fuel ash by employing Taguchi design and flower pollination algorithm
Published 2018“…In the meantime, both linear and interaction approach of FPA developed following optimal parameters: 5 wt.% of ground POFA, pressed at 4 t, and sintered at 1200 °C for 300 min of soaking time. …”
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Optimization of super twisting sliding mode control gains using Taguchi method
Published 2018“…This paper focuses on optimization of super twisting controller gains using Taguchi method with objective to minimize tracking error and the chattering effect. …”
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Metaheuristic techniques in enhancing the efficiency and performance of thermo-electric cooling devices
Published 2017“…The optimization findings obtained by using SA and DE are validated by applying them in some defined test cases taking into consideration non-linear inequality and non-linear equality constraint conditions. …”
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Composite nonlinear feedback control with multi-objective particle swarm optimization for active front steering system
Published 2024journal::journal article -
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Personalized administration of contrast medium with high delivery rate in low tube voltage coronary computed tomography angiography
Published 2019“…Methods: The algorithm for an IDR of 2.22 gI·s-1 was developed based on the relationship between VCE and contrast volume in 141 patients; test bolus parameters and characteristics in 75 patients; and, tube voltage in a phantom study. …”
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Adaptive model predictive control based on wavelet network and online sequential extreme learning machine for nonlinear systems
Published 2015“…Moreover, the ability of initialization the hidden nodes parameters using density function and recursive algorithm will help WN-OSELM to perform useful generalization facility and modeling accuracy. …”
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Evaluation of lightning return stroke current using measured electromagnetic fields
Published 2012“…This research proposed an inverse procedure algorithm using the proposed general fields’ expressions and the particle swarm optimization algorithm (PSO) in the time domain where the full channel base current wave shape in time domain can be determined. …”
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PREDICTIVE MAXIMUM POWER POINT TRACKING (MPPT) ALGORITHM FOR PERMANENT EXCHANGE MEMBRANE FUEL CELL (PEMFC)
Published 2022“…The focus of this project is to show the power characteristics extracted from the PEMFC system by using predictive control. A comparison of PEMFC performances based on the proposed technique with other existing MPPT algorithms will be done to validate the algorithm performance. …”
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Final Year Project Report / IMRAD -
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Development of cost reduction mathematical model for natural gas transmission network system
Published 2012“…Analysis of results illustrated the priority of the NGTSCM compared to the other design methods. Through one to one comparison of the costs of the networks, it was clear that, the costs, as calculated using the optimal method, were reduced by 2.91 % in first case, and 0.94 % in second case in comparison with another method. …”
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Comparison between specifications of linear regression and spatial-temporal autoregressive models in mass appraisal valuation for single storey residential property
Published 2013“…The best set of parameters that could produce more accurate results in the validation process, were selected and their associated neighbourhood and weights were used to compare with the linear models. …”
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