Individual scatter partition-based fuzzy neural networks using particle swarm optimisation
by Keon-Jun Park; Yong-Kab Kim; Byun-Gon Kim; Kwan-Woong Kim
International Journal of Sensor Networks (IJSNET), Vol. 15, No. 4, 2014

Abstract: This paper presents a new design of fuzzy neural networks (FNNs) based on individual scatter partition using particle swarm optimisation (PSO). The proposed FNNs are expressed by the scatter partition of input space generated by fuzzy c-means clustering algorithm. The partitioned local spaces indicate the fuzzy rules of the FNNs that have the individual regions in the different size. The consequence part of the rule is represented by polynomial functions. The back propagation algorithm is used to estimate the coefficients of the polynomial functions. The optimisation to find individual regions and parameters of learning is conducted by PSO. The performance of the proposed FNNs is demonstrated with the non-linear process.

Online publication date: Mon, 25-Aug-2014

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 Sensor Networks (IJSNET):
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