[1]YANG Chun-ling,WANG Jian-lai,ZHU Min.Using the improved particle swarm optimization to train the CNNE model[J].CAAI Transactions on Intelligent Systems,2007,2(3):67-72.
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Using the improved particle swarm optimization to train the CNNE model

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 2 Number of periods: 2007 3 Page number: 67-72 Column: 学术论文—机器学习 Public date: 2007-06-25
Title:
Using the improved particle swarm optimization to train the CNNE model
Author(s):
YANG Chun-ling; WANG Jian-lai ; ZHU Min
Electrical Engineering, Harbin Institute of Technology, Harbin , 150001, China
Keywords:
CNNE model;  particle swarm optimization;  gradient
CLC:
TP183
DOI:
-
Abstract:
An artificial intelligence algorithm - particle swarm optimization (PSO) were proposed to train the CNNE model. Aiming at the limitation of the sta ndard particle swarm optimization can be easily restricted in the local optimum point, a kind of particle swarm optimization(PSO)algorithm with gradient acceler ation is adopted by adding gradient information to influence the update of veloc ities of the particles. When the optimum information of the swarm is stagnant, s ome particles in the population are initialized again to reduce the possibility of trapping in local optimum. Comparing with the step steepest descent algorithm , using the particle swarm optimization algorithm to train the CNNE model improv es the speed of convergence of the algorithm on the premise of keeping precision , which solves the online realtime measurement of emissivity.
References:
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