Title: An integrated approach for modelling of multivariate data based on statistical principles and neural networks
Authors: Prasun Das
Addresses: SQC & OR Unit, Indian Statistical Institute, 203, B.T. Road, Kolkata 700 108, India
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.
Keywords: multicollinearity; integrated systems; feedforward networks; MARS; multivariate adaptive regression spline; PCNN; principal component neural networks; PCA; principal component analysis; multivariate data.
International Journal of Artificial Intelligence and Soft Computing, 2010 Vol.2 No.1/2, pp.132 - 143
Published online: 04 Apr 2010 *Full-text access for editors Access for subscribers Purchase this article Comment on this article