Quantum particle swarm optimisation based on chaotic mutation for automatic parameters determination of pulse coupled neural network Online publication date: Sat, 10-May-2014
by Linlin Mu; Maozheng Zhao; Chaozhu Zhang
International Journal of Computing Science and Mathematics (IJCSM), Vol. 4, No. 4, 2013
Abstract: Pulse coupled neural network (PCNN), a well-known class of neural networks, has original advantages when applied to image segmentation because of its biological background. However, when PCNN is used, the main problem is that its parameters are not self-adapting according to different image, which limits the application range of PCNN. Considering that, this paper proposed a new method based on quantum particle swarm optimisation (QPSO) and chaotic mutation to determine automatically the parameters of PCNN. In this method, the chaotic mutation-quantum particle swarm optimisation (CM-QPSO) is used to search automatically the optimal solution of the solution space of PCNN's parameters for image segmentation. Simulation results demonstrate that the proposed method is accurate and robust for image segmentation, and its performance is superior to the methods of GA and PSO when Shannon entropy is adopted as evaluation criteria.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
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 Computing Science and Mathematics (IJCSM):
Login with your Inderscience username and 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
Our Blog 
Follow us on Twitter 
Visit us on Facebook