PARAMETER ESTIMATION OF SCHOTTKY-BARRIER DIODE MODEL BY PARTICLE SWARM OPTIMIZATION

2009 ◽  
Vol 20 (05) ◽  
pp. 687-699 ◽  
Author(s):  
KAIER WANG ◽  
MEIYING YE
Keyword(s):  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Schottky Barrier ◽  
Particle Swarm ◽  
Schottky Barrier Diode ◽  
Estimation Accuracy ◽  
Parameter Estimation Problem ◽  
Swarm Optimization ◽  
Computation Efficiency ◽  
Parameter Values

This paper presents particle swarm optimization (PSO) method to solve the parameter estimation problem of the Schottky-barrier diode model. Based on the synthetic and experimental data, we have demonstrated that the proposed method has high parameter estimation accuracy. Besides, the initial guesses for the model parameter values are not required in the PSO method. Also, the performance of the PSO method is compared with that of the genetic algorithm (GA) method. The results indicate that the PSO method outperforms the binary-coded and real-coded GA methods in terms of estimation accuracy and computation efficiency.

Time-Frequency Analysis of Frequency Hopping Signals Based on Particle Swarm Optimization

2012 ◽  
Vol 195-196 ◽  
pp. 265-269
Author(s):  
Jian Tao Guo
Keyword(s):  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Frequency Hopping ◽  
Parameter Estimation Problem ◽  
Swarm Optimization ◽  
Time Frequency ◽  
Simulation Results ◽  
Blind Parameter Estimation

A new method is proposed for blind parameter estimation of frequency hopping signals. According to the relation between peaks location on the time frequency plane and component centers of frequency hopping signals, parameter estimation problem is solved using multi-species particle swarm optimization algorithm. Each particle moves around the time and frequency plane and will converge to different species, which species seed represents the center of frequency hopping component. Using this method, the parameters of frequency hopping signals can be estimated. Simulation results demonstrate that the method is effective and feasible.

scholarly journals Particle swarm optimization for the estimation of surface complexation constants with the geochemical model PHREEQC-3.1.2

2019 ◽  
Vol 12 (1) ◽  
pp. 167-177 ◽  
Author(s):  
Ramadan Abdelaziz ◽  
Broder J. Merkel ◽  
Mauricio Zambrano-Bigiarini ◽  
Sreejesh Nair
Keyword(s):  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Thermodynamic Parameter ◽  
Optimization Technique ◽  
Surface Complexation ◽  
Particle Swarm ◽  
Model Parameters ◽  
Swarm Optimization ◽  
Geochemical Model ◽  
Parameter Values

Abstract. Sorption of metals on minerals is a key process in treatment water, natural aquatic environments, and other water-related technologies. Sorption processes are usually simulated with surface complexation models; however, identifying numeric values for the thermodynamic constants from batch experiments requires a robust parameter estimation technique that does not get trapped in local minima. Recently, particle swarm optimization (PSO) techniques have attracted many researchers as an efficient and effective optimization technique to find (near-)optimum model parameters in several fields of research. In this work, uranium at low concentrations was sorbed on quartz at different pH, and the hydroPSO R optimization package was used – the first time – to calibrate the PHREEQC geochemical model, version 3.1.2. Results show that thermodynamic parameter values identified with hydroPSO are more reliable than those identified with the well-known parameter estimation (PEST) software, when both parameter estimation software are coupled to PHREEQC using the same thermodynamic input data. In addition, post-processing tools included in hydroPSO were helpful for the correct interpretation of uncertainty in the obtained model parameters and simulated values. Thus, hydroPSO proved to be an efficient and versatile optimization tool for identifying reliable thermodynamic parameter values of the PHREEQC geochemical model.

scholarly journals A Modified NM-PSO Method for Parameter Estimation Problems of Models

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
An Liu ◽  
Erwie Zahara ◽  
Ming-Ta Yang
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Wide Range ◽  
Estimation Problems

Ordinary differential equations usefully describe the behavior of a wide range of dynamic physical systems. The particle swarm optimization (PSO) method has been considered an effective tool for solving the engineering optimization problems for ordinary differential equations. This paper proposes a modified hybrid Nelder-Mead simplex search and particle swarm optimization (M-NM-PSO) method for solving parameter estimation problems. The M-NM-PSO method improves the efficiency of the PSO method and the conventional NM-PSO method by rapid convergence and better objective function value. Studies are made for three well-known cases, and the solutions of the M-NM-PSO method are compared with those by other methods published in the literature. The results demonstrate that the proposed M-NM-PSO method yields better estimation results than those obtained by the genetic algorithm, the modified genetic algorithm (real-coded GA (RCGA)), the conventional particle swarm optimization (PSO) method, and the conventional NM-PSO method.

Optimal Power Flow with TCSC and TCPS Modeling using Craziness and Turbulent Crazy Particle Swarm Optimization

2010 ◽  
Vol 1 (3) ◽  
pp. 34-50 ◽  
Author(s):  
P. K. Roy ◽  
S. P. Ghoshal ◽  
S. S. Thakur
Keyword(s):  
Particle Swarm Optimization ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Particle Swarm ◽  
Solution Quality ◽  
Swarm Optimization ◽  
Facts Devices ◽  
Computation Efficiency ◽  
Optimal Power ◽  
Real Coded Genetic Algorithm

This paper presents two new Particle swarm optimization methods to solve optimal power flow (OPF) in power system incorporating flexible AC transmission systems (FACTS). Two types of FACTS devices, thyristor-controlled series capacitor (TCSC) and thyristor controlled phase shifting (TCPS), are considered. In this paper, the problems of OPF with FACTS are solved by using particle swarm optimization with the inertia weight approach (PSOIWA), real coded genetic algorithm (RGA), craziness based particle swarm optimization (CRPSO), and turbulent crazy particle swarm optimization (TRPSO). The proposed methods are implemented on modified IEEE 30-bus system for four different cases. The simulation results show better solution quality and computation efficiency of TRPSO and CRPSO algorithms over PSOIWA and RGA. The study also shows that FACTS devices are capable of providing an economically attractive solution to OPF problems.

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