Parameters estimation online for Lorenz system by a novel quantum-behaved particle swarm optimization

2008 ◽  
Vol 17 (4) ◽  
pp. 1196-1201 ◽  
Author(s):  
Gao Fei ◽  
Li Zhuo-Qiu ◽  
Tong Heng-Qing
Keyword(s):  
Particle Swarm Optimization ◽  
Lorenz System ◽  
Particle Swarm ◽  
Parameters Estimation ◽  
Swarm Optimization

scholarly journals An Enhanced Segment Particle Swarm Optimization Algorithm for Kinetic Parameters Estimation of the Main Metabolic Model of Escherichia Coli

Processes ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 963
Author(s):  
Mohammed Adam Kunna ◽  
Tuty Asmawaty Abdul Kadir ◽  
Muhammad Akmal Remli ◽  
Noorlin Mohd Ali ◽  
Kohbalan Moorthy ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Kinetic Parameters ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Metabolic Model ◽  
Parameters Estimation ◽  
Swarm Optimization ◽  
Distance Minimization ◽  
Kinetic Parameters Estimation

Building a biologic model that describes the behavior of a cell in biologic systems is aimed at understanding the physiology of the cell, predicting the production of enzymes and metabolites, and providing a suitable data that is valid for bio-products. In addition, building a kinetic model requires the estimation of the kinetic parameters, but kinetic parameters estimation in kinetic modeling is a difficult task due to the nonlinearity of the model. As a result, kinetic parameters are mostly reported or estimated from different laboratories in different conditions and time consumption. Hence, based on the aforementioned problems, the optimization algorithm methods played an important role in addressing these problems. In this study, an Enhanced Segment Particle Swarm Optimization algorithm (ESe-PSO) was proposed for kinetic parameters estimation. This method was proposed to increase the exploration and the exploitation of the Segment Particle Swarm Optimization algorithm (Se-PSO). The main metabolic model of E. coli was used as a benchmark which contained 172 kinetic parameters distributed in five pathways. Seven kinetic parameters were well estimated based on the distance minimization between the simulation and the experimental results. The results revealed that the proposed method had the ability to deal with kinetic parameters estimation in terms of time consumption and distance minimization.

Self-Tuning Robust Fuzzy Controller Design Based on Multi-Objective Particle Swarm Optimization Adaptation Mechanism

2017 ◽  
Vol 139 (7) ◽  
Author(s):  
Edson B. M. Costa ◽  
Ginalber L. O. Serra
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Particle Swarm ◽  
Parameters Estimation ◽  
Recursive Least Squares ◽  
Adaptation Mechanism ◽  
Swarm Optimization ◽  
Adaptive Fuzzy ◽  
Multi Objective

In this paper, an adaptive fuzzy controller design methodology via multi-objective particle swarm optimization (MOPSO) based on robust stability criterion is proposed. The plant to be controlled is modeled from its input–output experimental data considering a Takagi–Sugeno (TS) fuzzy nonlinear autoregressive with exogenous input model, by using the fuzzy C-means clustering algorithm (antecedent parameters estimation) and the weighted recursive least squares (WRLS) algorithm (consequent parameters estimation). An adaptation mechanism as MOPSO problem for online tuning of a fuzzy model based digital proportional-integral-derivative (PID) controller parameters, based on the gain and phase margins specifications, is formulated. Experimental results for adaptive fuzzy digital PID control of a thermal plant with time-varying delay are presented to illustrate the efficiency and applicability of the proposed methodology.

scholarly journals Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Asrul Adam ◽  
Mohd Ibrahim Shapiai ◽  
Mohd Zaidi Mohd Tumari ◽  
Mohd Saberi Mohamad ◽  
Marizan Mubin
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Time Domain Analysis ◽  
Peak Detection ◽  
Parameters Estimation ◽  
Eeg Signals ◽  
Classification Rate ◽  
Swarm Optimization ◽  
Time Frequency

Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model.

scholarly journals Quadrotor Identification through the Cooperative Particle Swarm Optimization-Cuckoo Search Approach

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Nada El gmili ◽  
Mostafa Mjahed ◽  
Abdeljalil El kari ◽  
Hassan Ayad
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Dynamic Modelling ◽  
Cuckoo Search ◽  
Parameters Estimation ◽  
Model Parameters ◽  
Swarm Optimization ◽  
Social Thinking ◽  
Quadrotor Uav ◽  
Search Approach

This paper explores the model parameters estimation of a quadrotor UAV by exploiting the cooperative particle swarm optimization-cuckoo search (PSO-CS). The PSO-CS regulates the convergence velocity benefiting from the capabilities of social thinking and local search in PSO and CS. To evaluate the efficiency of the proposed methods, it is regarded as important to apply these approaches for identifying the autonomous complex and nonlinear dynamics of the quadrotor. After defining the quadrotor dynamic modelling using Newton–Euler formalism, the quadrotor model’s parameters are extracted by using intelligent PSO, CS, PSO-CS, and the statistical least squares (LS) methods. Finally, simulation results prove that PSO and PSO-CS are more efficient in optimal tuning of parameters values for the quadrotor identification.

Study on Dynamic Balance in Main Driving Mechanism of Cold Rolling Mill Based on the Particle Swarm Optimization Algorithm

2011 ◽  
Vol 55-57 ◽  
pp. 633-638 ◽  
Author(s):  
Wen Xian Tang ◽  
Jun Jie Sun ◽  
Bin Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Fitness Function ◽  
Dynamic Balance ◽  
Particle Swarm ◽  
Parameters Estimation ◽  
Inertia Force ◽  
Driving Mechanism ◽  
Swarm Optimization

A method for comprehensive dynamic balance of mechanism based on the particle swarm optimization is presented. This paper adopted nonlinear multi-objective programming method to carry out a study on three dynamic property indexes including inertia force, reaction of kinematic pair and input torque. Optimum solution for the parameters estimation problem based on the particle swarm optimization algorithm is obtained by constructing a fitness function of the mathematical optimization model, which consists of those property indexes. The simulation results indicate that the proposed method could eliminate the reluctant evaluations and interactions remarkably, thus improves the application's performance.

Robust Stability Self-Tuning Fuzzy PID Digital Controller

2018 ◽  
pp. 141-154
Author(s):  
Ginalber Luiz de Oliveira Serra ◽  
Edson B. M. Costa
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Control ◽  
Robust Stability ◽  
Clustering Algorithm ◽  
Adaptive Fuzzy Control ◽  
Particle Swarm ◽  
Parameters Estimation ◽  
Fuzzy Pid ◽  
Swarm Optimization ◽  
Self Tuning

A self-tuning fuzzy control methodology via particle swarm optimization based on robust stability criterion, is proposed. The plant to be controlled is modeled considering a Takagi-Sugeno (TS) fuzzy structure from input-output experimental data, by using the fuzzy C-Means clustering algorithm (antecedent parameters estimation) and weighted recursive least squares (WRLS) algorithm (consequent parameters estimation), respectively. An adaptation mechanism based on particle swarm optimization is used to tune recursively the parameters of a fuzzy PID controller, from the gain and phase margins specifications. Computational results for adaptive fuzzy control of a thermal plant with time varying delay is presented to illustrate the efficiency and applicability of the proposed methodology.

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