Conjugate direction particle swarm optimization based approach to determine kinetic parameters from differential scanning calorimetric data

2019 ◽  
Vol 676 ◽  
pp. 271-275 ◽  
Author(s):  
Hao Wang ◽  
Zichao Guo ◽  
Wanghua Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Kinetic Parameters ◽  
Particle Swarm ◽  
Differential Scanning Calorimetric ◽  
Calorimetric Data ◽  
Swarm Optimization ◽  
Conjugate Direction

scholarly journals Conjugate Direction Particle Swarm Optimization Based Approach to Determine Kinetic Parameters from Part of Adiabatic Data

2020 ◽  
Vol 11 (4) ◽  
pp. 82-88
Author(s):  
Xiao-Qiao Zhao ◽  
◽  
Hao Wang ◽  
Wen-Qian Wu ◽  
Wang-Hua Chen ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Kinetic Parameters ◽  
Particle Swarm ◽  
Local Extremum ◽  
Experimental Curve ◽  
Swarm Optimization ◽  
Detection Range ◽  
Conjugate Direction ◽  
Rise Rate ◽  
Stochastic Optimization Algorithm

Due to the limited detection range of the adiabatic equipment, it is difficult to get complete experimental curve of some materials and calculate the kinetic parameters. In this work, the conjugate direction particle swarm optimization (CDPSO) approach, as a global stochastic optimization algorithm, is proposed to estimate the kinetic parameters and complete experimental curve from part of adiabatic calorimetric data. This algorithm combines the conjugate direction algorithm (CD) which has the ability to escape from the local extremum and the global optimization ability of the particle swarm optimization (PSO) which finds the globally optimal solutions. One case was used to verify this method: 20% DTBP in toluene decompositions under adiabatic conditions. Comparing the experimental and calculated complete temperature curve, the accuracy of the fitted kinetic parameters calculated by no less than 70% temperature rise rate proportion of data is verified. The value of TD24 is well-deviated even used 10% proportion of data. The case reasonably proves the effectiveness of CDPSO algorithm in the estimation of kinetic parameters from part of adiabatic data.

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.

Estimation of kinetic parameters of an anaerobic digestion model using particle swarm optimization

2017 ◽  
Vol 120 ◽  
pp. 25-32 ◽  
Author(s):  
Jixiang Yang ◽  
Lunhui Lu ◽  
Wenjuan Ouyang ◽  
Yao Gou ◽  
Youpeng Chen ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Anaerobic Digestion ◽  
Kinetic Parameters ◽  
Particle Swarm ◽  
Swarm Optimization

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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