A hybrid training method of convolution neural networks using adaptive cooperative particle swarm optimiser
by Genfu Xiao; Huan Liu; Weian Guo; Lei Wang
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 16, No. 1, 2019

Abstract: For solving the problem that it is easy to fall into the local minimum in Convolution Neural Networks (CNN) training, a hybrid training algorithm based on heuristic algorithm is proposed. Firstly, an Adaptive Cooperative Particle Swarm Optimisation (ACPSO) is proposed, which uses a learning automata to adaptively divide the subpopulation of the Cooperative Particle Swarm Optimisation (CPSO), and makes the decision variables with strong coupling relationship enter the same subpopulation. Then, the connection weights of CNN are considered as elements in particles and the CNN is trained by ACPSO algorithm. The output of the ACPSO algorithm is applied as the initial weight of the BP algorithm for the purpose of speeding up the training speed of the CNN. The experimental results show that the ACPSO-BP algorithm has achieved good results, and the recognition rate of the CNN is improved. Thus it has the potential to be applied to other deep learning fields.

Online publication date: Mon, 21-Jan-2019

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Wireless and Mobile Computing (IJWMC):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com