Load identification of domestic induction heating based on Particle Swarm Optimization

2014 ◽  
Author(s):  
A. Dominguez ◽  
A. Otin ◽  
I. Urriza ◽  
L.A. Barragan ◽  
D. Navarro ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Induction Heating ◽  
Particle Swarm ◽  
Load Identification ◽  
Swarm Optimization

scholarly journals Velocity-Controlled Particle Swarm Optimization (PSO) and Its Application to the Optimization of Transverse Flux Induction Heating Apparatus

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 487 ◽  
Author(s):  
Youhua Wang ◽  
Bin Li ◽  
Liuxia Yin ◽  
Jiancheng Wu ◽  
Shipu Wu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Temperature Distribution ◽  
Induction Heating ◽  
Particle Swarm ◽  
Support Vector ◽  
Uniform Temperature ◽  
Swarm Optimization ◽  
Uniform Temperature Distribution ◽  
Transverse Flux ◽  
Better Than

The main disadvantage of transverse flux induction heating (TFIH) is its resulting non-uniform temperature distribution on the surface of the strip at the inductor outlet. For obtaining a uniform temperature distribution, an improved particle swarm optimization (PSO) named velocity-controlled PSO (VCPSO) is proposed and applied to optimize this problem. Support vector machine (SVM) is adopted to establish a regression model to replace the complex and time-consuming coupling calculation process involved in TFIH problem. Simulation results of several test functions show that VCPSO performs much better than standard PSO (SPSO). Moreover, based on the existing research and experiments, the application of VCPSO combined with SVM to the TFIH problem achieves satisfactory results.

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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