Search Results - (( data integration using algorithm ) OR ( (parameter OR parameters) simulation based algorithm ))
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Data-driven brain emotional learning-based intelligent controller-PID control of MIMO systems based on a modified safe experimentation dynamics algorithm
Published 2025“…The safe experimentation dynamics algorithm (SEDA) is one such method that optimizes controller parameters using data-driven techniques. …”
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Data-driven brain emotional learning-based intelligent controller-PID control of MIMO systems based on a modified safe experimentation dynamics algorithm
Published 2025“…The safe experimentation dynamics algorithm (SEDA) is one such method that optimizes controller parameters using data-driven techniques. …”
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Sequence and sequence-less t-way test suite generation strategy based on the elitist flower pollination algorithm
Published 2018“…In line with the emerging field called Search based Software Engineering, many recently developed t-way strategies have adopted meta-heuristic algorithms as the basis of their implementations such as Simulated Annealing, Genetic Algorithm, Ant Colony Optimization Algorithm, Particle Swarm Optimization, Harmony Search and Cuckoo Search, owing their superior performance in term of test size reduction as compared to general computational based strategies, such as General t-way, Test Vector Generator, In Parameter Order General, Jenny, and Automatic Efficient Test Generator. …”
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Enhancing reservoir simulation models with genetic algorithm optimized neural networks across diverse climatic zones / Saad Mawlood Saab
Published 2025“…The research improved the predictive models by integrating them with the Genetic Algorithm (GA). The optimizer algorithm (i.e., GA) determines the optimal input variables and internal parameters in the prediction models. …”
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Safe experimentation dynamics algorithm for data-driven PID controller of a class of underactuated systems
Published 2019“…Moreover, other than the memory-based feature, SED algorithm has fewer design parameters to be addressed and the independence of the gain sequence in the tuning process. …”
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Safe experimentation dynamics algorithm for data-driven PID controller of a class of underactuated systems
Published 2019“…Moreover, other than the memory-based feature, SED algorithm has fewer design parameters to be addressed and the independence of the gain sequence in the tuning process. …”
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Optimization of supply chain management by simulation based RFID with XBEE Network
Published 2015“…In order to solve this problem, a simulation based “Multi-Colony Global Particle Swarm Optimization (MC-GPSO)” algorithm was developed. …”
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Hierarchical multi-agent system in traffic network signalization with improved genetic algorithm
Published 2019“…A dynamic modeling technique is proposed using Q-learning (QL) algorithm to online observe and learn the inflow-outflow traffic behaviors and extract the model parameters to update the evaluation model used in the fitness function of genetic algorithm (GA). …”
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Proceedings -
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Application of nature-inspired algorithms and artificial intelligence for optimal efficiency of horizontal axis wind turbine / Md. Rasel Sarkar
Published 2019“…This study presents robust pitch angle control for the output wind power model in wide range wind speed by proportional-integral-derivative (PID) controller. In addition, ACO algorithm has been used for optimization of PID controller parameters to obtain within rated smooth output power of WT from fluctuating wind speed. …”
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System Identification And Control Of Automatic Car Pedal Pressing System For Low-Speed Driving In A Road Traffic Delay
Published 2022“…The controller gains were tuned using metaheuristic algorithm which is Particle Swarm Algorithm (PSO) for optimal values of fuzzy controller parameters. …”
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Undergraduates Project Papers -
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Mobility management schemes based on multiple criteria for optimization of seamless handover in long term evolution networks
Published 2014“…The third scheme works on the self optimization of handover parameters using fuzzy logic control (FLC) and multiple preparation (MP) called FuzAMP. …”
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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 -
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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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A Wavelet-Based Particle Swarm Optimization Algorithm for Digital Image Watermarking
Published 2012“…Furthermore, a novel algorithmic framework is proposed via a forecasted feasibility of the approach to parameters evaluation of hypothesized watermarked images in DWT domain. …”
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Modelling of Adsorption of Dyes from Aqueous Solution by Activated Carbon
Published 2004“…Programs written in FORTRAN 90 are used to extrapolate the mass transfer parameters by matching the simulation data with the experimental data of batch studies. …”
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Modeling the powder compaction process using the finite element method and inverse optimization
Published 2011“…First, it presents the implementation of the cap constitutive model into ABAQUS FE software using the closest point projection algorithm. Then, an inverse modeling procedure was proposed to alleviate the problems raised by the interpretation of the experimental tests and to more accurately determine the material parameters. …”
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Fuzzy clustering method and evaluation based on multi criteria decision making technique
Published 2018“…The proposed algorithm is used as a pre-processing method for data followed by Gustafson-Kessel (GK) algorithm to classify credit scoring data. …”
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Mobility management for seamless handover in carrier aggregation heterogeneous networks deployment scenario of long term evolution-advanced
Published 2018“…Secondly, a Hybrid Handover Parameters Optimization algorithm based on Enhanced Weight Performance (HHPO) is introduced to optimize, select suitable Handover Control Parameters (HCP) and to manage the conflict that may occur among self-optimization functions. …”
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