Search Results - (( simulation identification system algorithm ) OR ( parameter optimization method algorithm ))

Simultaneous computation of model order and parameter estimation for system identification based on opposition-based simulated Kalman filter by Badaruddin, Muhammad, Kamil Zakwan, Mohd Azmi, Zuwairie, Ibrahim, Ahmad Afif, Mohd Faudzi, Pebrianti, Dwi

“…Simultaneous Model Order and Parameter Estimation (SMOPE) has been proposed to address system identification problem efficiently using optimization algorithms. …”
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Optimal parameter estimation of permanent magnet synchronous motor by using Mothflame optimization algorithm / Abdolmajid Dejamkhooy and Sajjad Asefi by Dejamkhooy, Abdolmajid, Asefi, Sajjad

“…In the next step, the parameter identification as an optimization problem is solved by Moth-flame optimization, which is a novel nature-inspired heuristic algorithm. …”
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Hybrid intelligent methods for parameter identification and load frequency control in power system by Aqeel Sakhy, Jaber

“…For example, the classical methods for parameter identification (LSE and MLE), the classical methods for LFC (PI, PD and PID) and the intelligent methods (fuzzy logic, neural network, genetic algorithm, and PSO). …”
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Simulated real-time controller for tuning algorithm using modified hill climbing approach by Ahmed, Ahmed Abdulelah

“…Often, it is necessary to calibrate a certain parameters of a control system due to plant parameters fluctuation over time.In this research, an intelligent algorithmic tuning technique suitable for realtime system tuning based on hill climbing optimization algorithm and model reference adaptive control system (MRAC) technique is proposed. …”
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Hybridization of nonlinear sine cosine and safe experimentation dynamics algorithms for solving control engineering optimization problems by Mohd Helmi, Suid

“…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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Particle swarm optimization for NARX structure selection: application on DC motor model / Mohd Ikhwan Abdullah by Abdullah, Mohd Ikhwan

“…This thesis was presents the nonlinear identification of a DC motor using Binary Particle Swarm Optimization (BPSO) algorithm, as a model structure selection method, replacing the typical Orthogonal Least Squares (OLS) used in system identification. …”
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System identification and pid control of toothbrush simulator system by Mohd Yusoff, Ainul Husna

“…The selected PID parameters was from PID tuner tuning method with gain values of; Kp= 17.9287, Ki= 40.751 and Kd= -0.52705. …”
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Improving the modeling capacity of Volterra model using evolutionary computing methods based on Kalman Smoother adaptive filter by ., Edwar Yazid, Mohd Shahir Liew, Setyamartana Parman, Velluruzhati

“…The first step is combining the forward and backward estimator in the original Volterra model; the second step is reformulating the Volterra model into a state-space model so that the Kalman Smoother (KS) adaptive filter can be used to estimate the kernel coefficients; the third step is optimization of KS parameters using evolutionary computing algorithms such as particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC). …”
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Discrete-time system identification using genetic algorithm with single parent-based mating technique by Zainuddin, Farah Ayiesya

“…The findings showed that incorporating SPM with single-point crossover resulted in lower objective function (OF) values and improved error indices (EI) compared to traditional GA methods. This enhanced the GA's ability to avoid premature convergence and maintained a diverse solution set, leading to more optimal model selection. …”
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Improving the modeling capacity of Volterra model using evolutionary computing methods based on Kalman smoother adaptive filter by Yazid, E., Liew, M.S., Parman, S., Kurian, V.J.

“…The first step is combining the forward and backward estimator in the original Volterra model; the second step is reformulating the Volterra model into a state-space model so that the Kalman Smoother (KS) adaptive filter can be used to estimate the kernel coefficients; the third step is optimization of KS parameters using evolutionary computing algorithms such as particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC). …”
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Fuzzy modelling and control for a nonlinear reboiler system of a distillation column by Ibrahim, Mohd. Faisal

“…Model parameters are tuned using Genetic algorithm (GA) and Recursive least square (RLS). …”
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Adaptive model predictive control based on wavelet network and online sequential extreme learning machine for nonlinear systems by Salih, Dhiadeen Mohammed

“…The proposed methods applied with simulations for system identification of different nonlinear systems and had shown well capability of the LWN-OSELM and WN-OSELM over NN-OSELM in terms of modelling accuracy and fast convergence performance. …”
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Assessment of ANN-based auto-reclosing scheme developed on single machine-infinite bus model with IEEE 14-bus system model data by Fitiwi, D. Z., K., S. Rama Rao.

“…Consequently, improper reclosing of the line onto a fault is avoided. The fault identification prior to reclosing is based on optimized artificial neural network associated with three different training algorithms. …”
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Direct Adaptive Predictive Control For Wastewater Treatment Plant by Shair, Ezreen Farina, Abu Bakar, Norazhar, Mohd Nor, Arfah Syahida, Mohd Azam, Sazuan Nazrah, Mohd Sobran, Nur Maisarah, Zainal Abidin, Amar Faiz

“…The adaptive control structure is based on the linear model of the process and combined with numerical algorithm for subspace state space system identification (N4SID). …”
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Self-Adaptive Autoreclosing Scheme usingI Artificial Neural Network and Taguchi's Methodology in Extra High Voltage Transmission Systems by Desta, Zahlay Fitiwi

“…The fault identification prior to reclosing is based on optimized artificial neural network associated with three training algorithms, namely, Standard Error Back-Propagation, Levenberg Marquardt and Resilient Back-Propagation algorithms. …”
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Improved genetic algorithm for direct current motor high speed controller implemented on field programmable gate array by Alkhafaji, Falih Salih

“…There are many researches have been done to optimize PI controller based evolutionary algorithm, such as Genetic Algorithm (GA). …”
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Structural health monitoring using adaptive wavelet functions / Seyed Hossein Mahdavi by Seyed Hossein, Mahdavi

“…Accordingly, in the second part of this research, the procedure of structural identification and damage detection has been developed by employing the wavelet-based method through the modified genetic algorithms (GAs) to optimally solve inverse problems. …”
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Fault classification in smart distribution network using support vector machine by Chuan O.W., Ab Aziz N.F., Yasin Z.M., Salim N.A., Wahab N.A.

“…The developed algorithm is tested on IEEE 34 bus and IEEE 123 bus test distribution system. …”

PID controller based on bird mating optimizer for vibration cancellation of horizontal flexible plate by Muhammad Farith Nazreen, Abu Samah, Muhamad Sukri, Hadi, Abdul Malek, Abdul Wahab, Nadzirah, Mohd Mokhtar, Intan Zaurah, Mat Darus

“…Initially, input–output experiment data is collected from previous researchers and utilized to model system development. Then, the linear autoregressive with exogenous (ARX) model is selected as a model structure for model development using system identification method via bird mating optimizer (BMO) algorithm. …”
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System identification and control of linear electromechanical actuator using PI controller based metaheuristic approach by Abdullah Hashim, Azrul Azim, Abdul Ghani, Nor Maniha, Ahmad, Salmiah, Nasir, Ahmad Nor Kasruddin

“…While the PI controller is commonly used due to its simplicity and versatility in various control scenarios, its effectiveness is limited by its complex tuning process, which requires significant time and effort to achieve the optimal performance. To address this issue, the paper focuses on employing metaheuristic approaches that are Spiral Dynamic Algorithm (SDA) and Artificial Bee Colony (ABC) to fine tune the PI parameters for controlling the position of EMA. …”
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