Search Results - (( (variable OR variables) simulation based algorithm ) OR ( java application mining algorithm ))
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A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models
Published 2017“…The results show that the neural network optimized with genetic algorithm and trained with an optimally and intelligently selected input vector containing historical load and meteorological variables produced the best prediction accuracy. © 2017, UK Simulation Society. …”
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Direct approach for mining association rules from structured XML data
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Thesis -
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Robust diagnostics and variable selection procedure based on modified reweighted fast consistent and high breakdown estimator for high dimensional data
Published 2022“…The performance of the proposed method is illustrated using simulation study and on glass vessel data with 1920 variables, cardiomyopathy microarray data with 6319 variables, and octane data with 226 dimensions. …”
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Classification for large number of variables with two imbalanced groups
Published 2020“…Both simulated and real data sets were utilised to measure the performance of the proposed algorithms based on two evaluation indicators, sensitivity and specificity. …”
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Optimized clustering with modified K-means algorithm
Published 2021“…In dealing with correlated variables, PCA was embedded in the proposed algorithm. …”
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An improved particle swarm optimization based maximum power point tracking strategy with variable sampling time
Published 2015“…Unlike conventional PSO-based MPPT systems, a variable sampling time strategy (VSTS) based on the investigation of the dynamic behavior of converter current is deployed to increase system tracking time. …”
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Study of nature inspired computing (NIC) technique for optimal reactive power dispatch problems
Published 2017“…ORPD problem is a nonlinear optimization problem that involving both equality constraints and inequality constraints. The proposed algorithms are tested on five different case studies which are IEEE 30-bus system with 13 control variables, IEEE 30-bus system with 19 control variables, IEEE 30-bus system with 25 control variables, IEEE 57-bus system with 25 control variables and IEEE 118-bus system with 77 control variables. …”
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Research Report -
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Artificial Robust Control of Robot Arm: Design a Novel SISO Backstepping Adaptive Lyapunov Based Variable Structure Control.
Published 2011“…The fuzzy backstepping methodology is on-line tune the variable structure function based on adaptive methodology. …”
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Laser simulator logic: A novel inference system for highly overlapping of linguistic variable in membership functions
Published 2023“…It has been found that the current type 1 and 2 fuzzy logic are no longer suitable to deal with highly inference between linguistic variables. Thus, a novel algorithm, called laser simulator logic, is introduced in this paper to deal with the issue of highly inference of linguistic variables membership functions, which depends mainly on a proportional and relative-dynamic scale of membership function that will change the scale of membership continuously based on the crisp input/output. …”
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Maximizing power generation in variable speed micro-hydro with power point tracking
Published 2022“…The simulation results showed that the proposed GAbased P&O MPPT algorithm was able to track the global maximum power point (MPP).…”
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Proceedings -
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ENGINEERING DESIGN WITH PSO ALGORITHM
Published 2019“…Creating a PSO algorithm-based infrastructure integrating with the recommendation system will further enhance solution to the design problem. …”
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Final Year Project -
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Control of IC Engine: Design a Novel MIMO Fuzzy Backstepping Adaptive Based Fuzzy Estimator Variable Structure Control.
Published 2011“…The fuzzy controller in proposed fuzzy estimator variable structure controller is based on Lyapunov fuzzy inference system (FIS) with minimum model based rule base. …”
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Neural network based model predictive control for a steel pickling process
Published 2009“…The Levenberg-Marquardt algorithm is used to train the process models. In the control (MPC) algorithm, the feedforward neural network models are used to predict the state variables over a prediction horizon within the model predictive control algorithm for searching the optimal control actions via sequential quadratic programming. …”
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A hybrid heuristic of variable neighbourhood descent and great deluge algorithm for efficient task scheduling in grid computing
Published 2019“…In this paper, we propose a novel hybrid heuristic-based algorithm, which synergised the excellent diversification capability of Great Deluge (GD) algorithm with the powerful systematic multi-neighbourhood search strategy captured in Variable Neighbourhood Descent (VND) algorithm, to efficiently schedule independent tasks in Grid computing environment with an objective of minimising the makespan. …”
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Automatic control of flotation process using computer vision
Published 2015“…A froth model correlating the image variables to process variables and a prediction system estimating the metallurgical parameters based on image variables were then developed by using a neural network structure. …”
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Thesis -
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Design optimization of valve timing at various engine speeds using Multi-Objective Genetic Algorithm (MOGA)
Published 2008“…Majority of the research works in this project were in CAE software environment and method to implement optimization to 1D engine simulation. The paper conducts design optimization of CAMPRO 1.6L (S4PH) engine valve timing at various engine speeds using multi-objective genetic algorithm (MOGA) for the future variable valve timing (VVT) system research and development. …”
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Proceeding Paper
