Search Results - (( data application tools algorithm ) OR ( parameter optimization method algorithm ))
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1
Safe experimentation dynamics algorithm for data-driven PID controller of a class of underactuated systems
Published 2019“…Hence, a memory-based optimization tool has good potential to retain the optimal design parameter during the PID tuning process. …”
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2
Safe experimentation dynamics algorithm for data-driven PID controller of a class of underactuated systems
Published 2019“…Hence, a memory-based optimization tool has good potential to retain the optimal design parameter during the PID tuning process. …”
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3
Modeling and multi-objective optimal sizing of standalone photovoltaic system based on evolutionary algorithms
Published 2020“…Due to the effective attraction-repulsion mechanism of electromagnetic-like (EM) algorithm and reliable exploration and exploitation phases of differential evolution (DE), these two methods were used to determine parameters of the single diode PV model and finding optimal sizing of the SAPV system. …”
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4
Hybridization of nonlinear sine cosine and safe experimentation dynamics algorithms for solving control engineering optimization problems
Published 2024“…The empirical assessment of these proposed methods encompasses a diverse set of 23 benchmark functions, demonstrating their efficacy comparable to well-established metaheuristic algorithms such as as the Grey Wolf Optimizer (GWO), Multi-Verse Optimization (MVO), Sine Cosine Algorithm (SCA), Ant Lion Optimizer (ALO), Moth-Flame Optimization Algorithm (MFO), and Grasshopper Optimization Algorithm (GOA). …”
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5
Robust Portfolio Mean-Variance Optimization for Capital Allocation in Stock Investment Using the Genetic Algorithm: A Systematic Literature Review
Published 2024journal::journal article -
6
A decision support system for improving forecast using genetic algorithm and tabu search
Published 2008“…Hence, a good optimization technique is required to select the best parameter value to minimize the fitness function. …”
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Article -
7
An Improved Grasshopper Optimization Algorithm Based Echo State Network for Predicting Faults in Airplane Engines
Published 2020“…This problem is often formulated as a typical optimization problem. Metaheuristic algorithms are known to be excellent tools for solving optimization problems. …”
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8
Assisted History Matching by Using Genetic Algorithm and Discrete Cosine Transform
Published 2014“…Overall, an algorithm showing the combination method of both GA and DCT was successfully developed. …”
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Final Year Project -
9
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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10
Data-driven PID controller of wind turbine systems using safe experimentation dynamics algorithm
Published 2024“…Recent interest in optimizing PID control parameters offers advantages in output response enhancement while preserving robustness and simplicity. …”
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11
Machine Learning Based Optimal Design of On-Road Charging Lane for Smart Cities Applications
Published 2025“…The algorithm not only aids in estimating the infrastructure cost of the charging lane but also predicts optimal design parameters using trained data. …”
Article -
12
Combining Recursive Least Square and Principal Component Analysis for Assisted History Matching
Published 2014“…This project will study the applicability of the combined algorithm for history matching problem. …”
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Final Year Project -
13
Classification of Google Play application using decision tree algorithm on sentiment analysis of text reviews / Aqil Khairy Hamsani, Ummu Fatihah Mohd Bahrin and Wan Dorishah Wan A...
Published 2023“…To achieve these objectives, the methods employed involve data preprocessing and implementing the Decision Tree (DT) algorithm for classification. …”
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14
Development of tool life prediction model of TiAlN coated tools during the high speed hard milling of AISI H13 steel
Published 2011“…Öktem et al. [6] incorporated RSM with developed genetic algorithm to optimize cutting parameters for better surface quality in case of Inconel 718. …”
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Book Chapter -
15
Producer gas composition prediction using artificial neural network algorithm / Mohd Mahadzir Mohammud ... [et al.]
Published 2023“…The algorithm is trained, tested, and verified with the experiment data. …”
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16
Case study : an effect of noise in character recognition system using neural network
Published 2003“…This projects uses the most popular training method in character recognition problem, namely backpropagation algorithm. …”
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17
Development of data-driven controller for slosh suppression in liquid cargo vehicles
“…Here, a Safe Experimentation Dynamics (SED) algorithm is suggested as a promising tool for the data-driven control approach. …”
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Research Report -
18
Data-driven neuroendocrine-pid tuning based on safe experimentation dynamics for control of TITO coupled tank system with stochastic input delay
Published 2019“…This paper addresses a data-driven neuroendocrine-PID tuning for the control of a two-input-two-output (TITO) coupled tank system with sto-chastic input time delay based on safe experimentation dynamics (SED). The SED algorithm is an optimization method used as data-driven tools to find the optimal control parameters by using the input-output (I/O) data measurement in an actual system. …”
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Conference or Workshop Item -
19
Development of a hybrid PSO-ANN model for estimating glucose and xylose yields for microwave-assisted pretreatment and the enzymatic hydrolysis of lignocellulosic biomass
Published 2018“…ANN is a powerful tool capable of determining the relationship between the desired input and output data while PSO was used as a robust population-based search algorithm to optimize the performance of the ANN model. …”
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Article -
20
Discrete-time system identification using genetic algorithm with single parent-based mating technique
Published 2024“…The SPM technique offers researchers and practitioners a powerful tool for achieving faster convergence, better optimization, and more accurate models. …”
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