Search Results - (( data identification using algorithm ) OR ( parameter simulation based algorithm ))
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1
Simultaneous Computation of Model Order and Parameter Estimation for System Identification Based on Gravitational Search Algorithm
Published 2015“…In this paper, a technique termed as Simultaneous Model Order and Parameter Estimation (SMOPE), which is specifically based on Gravitational Search Algorithm (GSA) is proposed to combine model order selection and parameter estimation in one process. …”
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2
Identification of hammerstain model using stochastic perturbation simultaneous approximation
Published 2016“…The SPSA based method is then used to estimate the parameters in both the linear and non-linear parts based on the given input and output data with the present of delay in time. …”
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3
Optimization of supply chain management by simulation based RFID with XBEE Network
Published 2015“…It is an advanced Auto-ID wireless network based configuration system used for identification and tracking of items movement data. …”
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4
A graphical user interface application for continuous-time identification of dynamical system
Published 2002“…The GUI based on MATLAB consists of data preprocessing, parameter estimation, model validation and model simulation. …”
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5
Improving the modeling capacity of Volterra model using evolutionary computing methods based on Kalman Smoother adaptive filter
Published 2015“…The applicability of the proposed methods is tested in three simulated data and one experimental data. The results show that Volterra model with PSO–KS is preferable for fast identification process, while ABC–KS method is preferable for accurate identification process. …”
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6
Parameter Magnitude-Based Information Criterion For Optimum Model Structure Selection In System Identification
Published 2020“…For simulated data, PMIC2 was compared to AIC and BIC using enumerative approach and genetic algorithm. …”
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7
Wavelet network based online sequential extreme learning machine for dynamic system modeling
Published 2013“…The proposed model used as system identification for nonlinear dynamic systems. …”
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8
Kalman filter based impedance parameter estimation for transmission line and distribution line
Published 2019“…Most of the current algorithms for parameter identification are based on the assumption that transmission line is fully transposed and the corresponding components are symmetric. …”
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9
Improving the modeling capacity of Volterra model using evolutionary computing methods based on Kalman smoother adaptive filter
Published 2015“…The applicability of the proposed methods is tested in three simulated data and one experimental data. The results show that Volterra model with PSO-KS is preferable for fast identification process, while ABC-KS method is preferable for accurate identification process. …”
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10
Taguchi's method for optimized neural network based autoreclosure in extra high voltage lines
Published 2008“…The algorithms are developed using MATLABTM software. A range of faults are simulated using SimPowerSytemsTM and the spectra of the fault data are analyzed using Fast Fourier Transform which facilitates extraction of distinct features of each fault type. …”
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11
Discrete-time system identification using genetic algorithm with single parent-based mating technique
Published 2024“…Performance indicators included the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Parameter Magnitude-based Information Criterion 2 (PMIC2). …”
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12
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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13
Adaptive model predictive control based on wavelet network and online sequential extreme learning machine for nonlinear systems
Published 2015“…The ability of wavelets for localization in both time and frequency domain will help OSELM to train the WN in both uniform and non-uniform data sets. Moreover, the ability of initialization the hidden nodes parameters using density function and recursive algorithm will help WN-OSELM to perform useful generalization facility and modeling accuracy. …”
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14
Fuzzy modelling and control for a nonlinear reboiler system of a distillation column
Published 2006“…Model parameters are tuned using Genetic algorithm (GA) and Recursive least square (RLS). …”
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15
Modeling the powder compaction process using the finite element method and inverse optimization
Published 2011“…The simulation and identification module developed was applied to simulate the compaction of some industrial parts. …”
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16
Neural network based adaptive pid controller for shell-and-tube heat exchanger
Published 2019“…In this study, neural network approach was introduced to auto-tune the controller parameters. The dynamic data from the BDT921 plant was collected to formulate the mathematical model of the process using MATLAB System Identification Toolbox. …”
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17
Neural network based adaptive pid controller for shell-and-tube heat exchanger: article
Published 2019“…In this study, neural network approach was introduced to auto-tune the controller parameters. The dynamic data from the BDT921 plant was collected to formulate the mathematical model of the process using MATLAB System Identification Toolbox. …”
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18
Modified least trimmed squares method for face recognition / Nur Azimah Abdul Rahim
Published 2018“…The genetic algorithm configuration for n (number of observations) and p (parameter) was changed to assess the performance of modified method. …”
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19
Improved genetic algorithm for direct current motor high speed controller implemented on field programmable gate array
Published 2019“…There are many researches have been done to optimize PI controller based evolutionary algorithm, such as Genetic Algorithm (GA). …”
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20
Self-Adaptive Autoreclosing Scheme usingI Artificial Neural Network and Taguchi's Methodology in Extra High Voltage Transmission Systems
Published 2009“…In addition, Taguchi's methodology is employed in optimizing the parameters of each algorithm used for training, and in deciding the number of hidden neurons of the neural network. …”
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