On selecting the MIMO Generalized Orthonormal Basis Functions poles by using Particle Swarm Optimization

2007 ◽  
Author(s):  
Bruno C. Reginato ◽  
Gustavo H. C. Oliveira
Keyword(s):  
Particle Swarm Optimization ◽  
Orthonormal Basis ◽  
Particle Swarm ◽  
Basis Functions ◽  
Swarm Optimization

Recursive Particle Swarm Optimization Application for the Radial Basis Function Networks Modeling System

2015 ◽  
Vol 713-715 ◽  
pp. 1817-1820
Author(s):  
Ling Liu ◽  
Min Chen ◽  
Hong Yi Guo
Keyword(s):  
Particle Swarm Optimization ◽  
Radial Basis Function ◽  
Basis Function ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Basis Functions ◽  
Radial Basis Function Networks ◽  
Historical Knowledge ◽  
Swarm Optimization ◽  
Radial Basis

A Recursive Particle Swarm Optimization (RPSO) is proposed to solve dynamic optimization problems where the data is obtained not once but one by one. The position of each particle swarm is updated recursively based on the continuous data and the historical knowledge. The experiment results indicate that RPSO-based radial basis function networks needs fewer radial basis functions and gives more accurate results than traditional PSO in solving dynamic problems.

scholarly journals Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and Particle Swarm Optimization

2013 ◽  
Vol 225 (3) ◽  
pp. 528-540 ◽  
Author(s):  
Georgios Sermpinis ◽  
Konstantinos Theofilatos ◽  
Andreas Karathanasopoulos ◽  
Efstratios F. Georgopoulos ◽  
Christian Dunis
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Exchange Rates ◽  
Radial Basis Functions ◽  
Foreign Exchange ◽  
Particle Swarm ◽  
Basis Functions ◽  
Foreign Exchange Rates ◽  
Swarm Optimization ◽  
Radial Basis

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

Adaptive Inertia Weight Particle Swarm Optimization For Linear and Nonlinear Channel Equalization

2012 ◽  
Vol 3 (4) ◽  
pp. 1-4
Author(s):  
Diana D.C Diana D.C ◽  
◽  
Joy Vasantha Rani.S.P Joy Vasantha Rani.S.P ◽  
Nithya.T.R Nithya.T.R ◽  
Srimukhee.B Srimukhee.B
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Channel Equalization ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Linear And Nonlinear
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