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1
Optimum grouping in a modified genetic algorithm for discrete-time, non-linear system identification
Published 2007“…The issue of model parsimony is also addressed, and the model is validated using correlation tests. …”
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2
Optimum grouping in a modified genetic algorithm for discrete-time, non-linear system identification
Published 2007“…The issue of model parsimony is also addressed, and the model is validated using correlation tests. …”
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3
Optimization and control of hydro generation scheduling using hybrid firefly algorithm and particle swarm optimization techniques
Published 2018“…Secondly, this approach hybridizing the FA with the rough algorithm (RA), where RA is used to control the steps of randomness for the FA while optimizing the weights of the standard BPNN model. …”
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4
A concentration prediction-based crop digital twin using nutrient co-existence and composition in regression algorithms
Published 2024“…Crop digital twin is redefining traditional farming practices, offering unprecedented opportunities for real-time monitoring, predictive and simulation analysis, and optimization. This research embarks on an exploration of the synergy between precision agriculture, crop modeling, and regression algorithms to create a digital twin for farmers to augment the concentration and composition prediction-based crop nutrient recovery. …”
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5
An integrated reservoir modelling and evolutionary algorithm for optimizing field development in a mature fractured reservoir
Published 2016“…Examining two different optimization approaches used in this work, the genetic algorithm program gave results similar to the results that were obtained by an exhaustive method with much less computation time which is a great issue mainly for large size fields or fields which possess condensate gas and require the use of compositional simulators. …”
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6
Optimum grouping in a modified genetic algorithm for discrete-time, non-linear system identification
Published 2007“…The issue of model parsimony is also addressed, and the model is validated using correlation tests. …”
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7
A concentration prediction-based crop digital twin using nutrient co-existence and composition in regression algorithms
Published 2024“…Crop digital twin is redefining traditional farming practices, offering unprecedented opportunities for real-time monitoring, predictive and simulation analysis, and optimization. This research embarks on an exploration of the synergy between precision agriculture, crop modeling, and re-gression algorithms to create a digital twin for farmers to augment the concentration and compo-sition prediction-based crop nutrient recovery. …”
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8
Modeling of static and dynamic components of bio-nanorobotic systems
Published 2012“…Agent-based structural models of the nanomotors are proposed using composite diagram of Unified Modeling Language (UML) to introduce the sensors and actuators of the nanomotors. …”
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Thesis -
9
DEVELOPMENT OF COMPOSITIONAL MODEL FOR PREDICTING VISCOSITY OF CRUDE OILS USING POLYNOMIAL NEURAL NETWORKS (PNN) INDUCED BY GROUP METHOD OF DATA HANDLING (GMDH)
Published 2011“…GMDH is an inductive algorithm for computer-based mathematical modeling using neural network with active neurons that optimizes model coefficients for predetermine mathematical equation and selects the optimal model complexity. …”
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Final Year Project -
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Decentralized Adaptive Pi With Adaptive Interaction Algorithm Of Wastewater Treatment Plant
Published 2014“…The error function is minimized directly by approximate Frechet tuning algorithm without explicit estimation of the model. …”
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12
Multi-parametric optimization of aerodynamic performance and pedestrian crash in sedan front-end profiles
Published 2025“…A multiobjective optimization framework was developed using computational simulations, mathematical modelling, and evolutionary algorithms. …”
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13
Parametric analysis of critical buckling in composite laminate structures under mechanical and thermal loads: a finite element and machine learning approach
Published 2024“…Later, various structural and material parameters like spacing ratio, opening ratio, hole shape, fiber orientation, and laminate sequence are systematically varied. Subsequently, simulation data from numerous cases are utilized to identify the best parameter combination using machine learning algorithms. …”
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14
Profit Optimization of a Refrigerated Gas Plant
Published 2007“…This requires continuous re-evaluation of the operating conditions through an efficient optimization algorithm. The success of solving the optimization problem rests on the accuracy of the plant model used, as well as the inclusion of technical and business aspects of the plant operation. …”
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15
Profit Optimization of a Refrigerated Gas Plant
Published 2007“…This requires continuous re-evaluation of the operating conditions through an efficient optimization algorithm. The success of solving the optimization problem rests on the accuracy of the plant model used, as well as the inclusion of technical and business aspects of the plant operation. …”
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16
Profit Optimization of a Refrigerated Gas Plant
Published 2007“…This requires continuous re-evaluation of the operating conditions through an efficient optimization algorithm. The success of solving the optimization problem rests on the accuracy of the plant model used, as well as the inclusion of technical and business aspects of the plant operation. …”
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Modeling And Optimization Of Lipase-Catalyzed Synthesis Of Adipate Esters Using Response Surface Methodology And Artificial Neural Network
Published 2010“…Various feedforward neural networks were performed using different learning algorithms. The best algorithm was found to be Levenberg–Marquardt (LM) for a network composed of seven hidden nodes with hyperbolic tangent sigmoid transfer function. …”
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Thesis -
19
Forecasting hydrological parameters for reservoir system utilizing artificial intelligent models and exploring their influence on operation performance
Published 2019“…The three different optimization algorithms used in this study are the genetic algorithm (GA), particle swarm optimization (PSO) algorithm and shark machine learning algorithm (SMLA). …”
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A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models
Published 2017“…Genetic algorithm and simulated annealing techniques are used to optimize the control parameters of the neural network. …”
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