A Modified PSO Learning Algorithm for PID Neural Network

Author

Li Ming ; Yang Chengwu

Author_Institution

Coll. of Power Eng., Nanjing Univ. of Sci. & Techonology, China

fYear

2006

fDate

7-11 Aug. 2006

Firstpage

1123

Lastpage

1125

Abstract

Traditional PID neural network adopts BP learning algorithm. However, without accurate gradients, its initial MSE is too large and the procedure of convergence may be unstable. A modified PSO (MPSO) algorithm is introduced to training the PID neural network. The MPSO algorithm does not need any gradient information. It can keep large variety all along and solve premature convergence, which is a major problem in basic PSO algorithm. Simulation results show MPSO algorithm is the best learning algorithm for PID neural network.

Keywords

learning (artificial intelligence); neurocontrollers; particle swarm optimisation; three-term control; BP learning; PID neural network; modified PSO learning; premature convergence; Artificial intelligence; Convergence; Educational institutions; Electronic mail; MATLAB; Neural networks; Power engineering; Three-term control; PID neural network; PSO algorithm; learning algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference, 2006. CCC 2006. Chinese

Conference_Location

Harbin

Print_ISBN

7-81077-802-1

Type

conf

DOI

10.1109/CHICC.2006.280575

Filename

4060254

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3210529