Search Results - (( parallel optimization sensor algorithm ) OR ( parameter simulation approach algorithm ))*
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1
Simulated kalman filter (SKF) based image template matching for distance measurement by using stereo vision system
Published 2018“…Stereo vision sensor consists of two stereo cameras, mounted parallel in stationary position. …”
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2
Simulation algorithm of bayesian approach for choice-conjoint model
Published 2011“…As we know, the Multinomial Probit Model (MPM) is a method which assumes that chosen observations are independent but according to researchers the MPM is rarely used due to computational difficulties in computing the maximum likelihood estimates (MLE) for estimate MPM parameters. Therefore this research propose simulation algorithm of Bayesian approach for estimating parameter in MPM by Bayesian analysis to avoid computational difficulties in computing the maximum likelihood estimates (MLE).…”
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3
A simulation based fly optimisation algorithm for swarms of mini autonomous surface vehicles application
Published 2011“…The optimal simulation parameters can be used for the real application. …”
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4
Optimization of turning parameters using genetic algorithm method
Published 2008“…The simulation based on Genetic Algorithm are successful develop and the optimum parameters values are obtained from the simulation.…”
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Undergraduates Project Papers -
5
Single-objective and multi-objective optimization algorithms based on sperm fertilization procedure / Hisham Ahmad Theeb Shehadeh
Published 2018“…The obtained results are compared with the results of four algorithms. These algorithms are Genetic Algorithms (GA), Parallel Genetic Algorithm (PGA), Particle Swarm Optimization (PSO) and Accelerated Particle Swarm Optimization (APSO). …”
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6
An Intelligent Voltage Controller for a PV Inverter System Using Simulated Annealing Algorithm-Based PI Tuning Approach
Published 2017“…This study associates an intelligent voltage controller based PI approach for PV electrical inverter by employing a meta-heuristic optimization algorithmic called a Simulated Annealing (SA) algorithm. …”
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7
Characterization of PV panel and global optimization of its model parameters using genetic algorithm
Published 2023“…This paper details an improved modeling technique for a photovoltaic (PV) module; utilizing the optimization ability of a genetic algorithm, with different parameters of the PV module being computed via this approach. …”
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Characterization of PV panel and global optimization of its model parameters using genetic algorithm
Published 2013“…This paper details an improved modeling technique for a photovoltaic (PV) module; utilizing the optimization ability of a genetic algorithm, with different parameters of the PV module being computed via this approach. …”
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9
Performance monitoring algorithm for optimizing electrical power generated by using photovoltaic system
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Conference or Workshop Item -
10
Hybrid meta-heuristic algorithm for solving multi-objective aggregate production planning in fuzzy environment
Published 2017“…During the course of the present work, two fuzzy methods (modified Zimmermanns approach and modified angelovs approach ) and fourmeta-heuristics and hybrid meta heuristics including; simulated annealing (SA), modified simulated annealing (MSA), hybrid modified simulated annealing and simplex downhill (MSASD), hybrid modified simulated annealing and modified particle swarm optimization (MSAPSO) were proposed. …”
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11
A multiobjective simulated Kalman filter optimization algorithm
Published 2018“…It is a further enhancement of a single-objective Simulated Kalman Filter (SKF) optimization algorithm. …”
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Conference or Workshop Item -
12
Single-Solution Simulated Kalman Filter Algorithm for Global Optimisation Problems
Published 2016“…In comparison, the proposed ssSKF algorithm supersedes the original population-based Simulated Kalman Filter (SKF) algorithm by operating with only a single agent, and having less parameters to be tuned. …”
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13
African Buffalo Optimization Algorithm for Tuning Parameters of a PID Controller in Automatic Voltage Regulators
Published 2016“…The simulation outcome of the application of ABO to tune the parameters of a PID-Controller parameters of Automatic Voltage Regulators has been very competitive when compared similar outcomes of other metaheuristics tuners: BFO-PID, PSO-PID, GA-PID, PID-PSO, PID Tuner and ACO-PID.…”
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14
Single-solution Simulated Kalman Filter algorithm for global optimisation problems
Published 2018“…In comparison, the proposed ssSKF algorithm supersedes the original population-based Simulated Kalman Filter (SKF) algorithm by operating with only a single agent, and having less parameters to be tuned. …”
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15
Single-solution Simulated Kalman Filter algorithm for global optimisation problems
Published 2018“…In comparison, the proposed ssSKF algorithm supersedes the original population-based Simulated Kalman Filter (SKF) algorithm by operating with only a single agent, and having less parameters to be tuned. …”
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16
Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm
Published 2025“…This approach demonstrates the ability of the GOOSE algorithm to simulate complex systems and enhances the robustness and adaptability of the simulation tool by integrating essential behaviours into the computational framework. …”
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Cognitive map approach for mobility path optimization using multiple objectives genetic algorithm
Published 2023Subjects:Conference Paper -
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PROPOSED METHODOLOGY FOR OPTIMIZING THE TRAINING PARAMETERS OF A MULTILAYER FEED-FORWARD ARTIFICIAL NEURAL NETWORKS USING A GENETIC ALGORITHM
Published 2011“…In this thesis, the approach has been analyzed and algorithms that simulate the new approach have been mapped out.…”
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19
A multi-objective parametric algorithm for sensor-based navigation in uncharted terrains
Published 2023“…Simulation studies have shown the algorithm to perform robust and effective in all environments.…”
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A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models
Published 2017“…These models are developed by integrating multilayer perceptron neural network and evolutionary optimization techniques. Genetic algorithm and simulated annealing techniques are used to optimize the control parameters of the neural network. …”
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