Innovative quality process control via cellular neural networks
by Leonarda Carnimeo, Michele Dassisti
International Journal of Technology Management (IJTM), Vol. 37, No. 1/2, 2007

Abstract: The continuous detection and correction of unnatural process behaviours, owing to special causes of variations, is a basic task in manufacturing to keep any process stable and predictable, as well as to improve it continuously. In this paper, a contribution to increase quality process control from an innovative point of view is presented, which consists in designing Cellular Neural Networks (CNNs) to behave as associative memories for recognising unnatural behaviours. As an example, a test case is developed by considering abnormal cyclic behaviours given by sinusoidal signals. For this purpose, a CNN is synthesised for an associative memory, to recognise these unnatural situations. A robustness analysis of the synthesised network is then developed in the presence of input noise. The performances of the designed circuit have been illustrated in detail.

Online publication date: Sat, 23-Dec-2006

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 Technology Management (IJTM):
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