An integrated approach for modelling of multivariate data based on statistical principles and neural networks
by Prasun Das
International Journal of Artificial Intelligence and Soft Computing (IJAISC), Vol. 2, No. 1/2, 2010

Abstract: For multivariate systems, empirical modelling using statistical methods is an issue in the presence of multicollinearity. Recently, neural networks provide a wide class of general-purpose, flexible non-linear non-parametric mapping, ignoring the fact of underlying data property. In this work, a composite system is designed to integrate the statistical principles of multivariate data, particularly multicollinearity, into the neural network architecture. The designed system is trained and validated with a simulated database based on the extent of correlation structure among the input variables. A real-life case with a large multi-dimensional system is illustrated under the proposed system.

Online publication date: Sun, 04-Apr-2010

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 Artificial Intelligence and Soft Computing (IJAISC):
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