Paper Info
Title
On robust nonlinear modeling of a complex process with large number of inputs using m-QRcp factorization and Cp statistic
Abstract
The problem of modeling complex processes with a large number of inputs is addressed. A new method is proposed for the optimization of the models in minimum Cp statistic sense using QR with a modified scheme of column pivoting (m-QRcp) factorization. Two different classes of multilayer nonlinear modeling problems are explored: 1) in the first class of models, each layer comprises multiple linearly parameterized submodels or cells; the individual cells are optimally modeled using QR factorization, and m-QRcp factorization ensures optimal selection of variables across the layers. 2) The nonhomogeneous feed-forward neural network is chosen as the second class of models, where the network architecture and structure are optimized in terms of best set of hidden links (and nodes) using m-QPcp factorization. In both the cases, the optimization is shown to be direct and conclusive. The proposed is a generic approach to the optimal modeling of complex multilayered architectures, which leads to computationally fast and numerically robust parsimonious designs, free from collinearity problems. The method is largely free from heuristics and is amenable to automated modeling
Year
DOI
Venue
1999
10.1109/3477.740161
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Keywords
DocType
Volume
large-scale systems,modelling,multilayer perceptrons,Cp statistic,complex multilayered architectures,complex process,feed-forward neural network,large-scale systems,m-QRcp factorization,model reduction,neural networks,nonlinear models,optimal modeling,orthogonal transformation,partial least-squares,robust nonlinear modeling,subset selection
Journal
29
Issue
ISSN
Citations 
1
1083-4419
2
PageRank 
References 
Authors
0.38
13
3
Name
Order
Citations
PageRank
P P Kanjilal16619.54
Goutam Saha225523.17
Thomas Jacob Koickal3446.55