Input Dimension Reduction in Neural Network Training-Case Study in Transient Stability Assessment of Large Systems
This document has been relocated to http://scholarsmine.mst.edu/ele_comeng_facwork/1682
There were 10 downloads as of 28 Jun 2016.
Abstract
The problem in modeling large systems by artificial neural networks (ANN) is that the size of the input vector can become excessively large. This condition can potentially increase the likelihood of convergence problems for the training algorithm adopted. Besides, the memory requirement and the processing time also increase. This paper addresses the issue of ANN input dimension reduction. Two different methods are discussed and compared for efficiency and accuracy when applied to transient stability assessment.
This paper has been withdrawn.
