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.
Recommended Citation
S. Muknahallipatna and B. H. Chowdhury, "Input Dimension Reduction in Neural Network Training-Case Study in Transient Stability Assessment of Large Systems," Proceedings of the International Conference on Intelligent Systems Applications to Power Systems, 1996, Institute of Electrical and Electronics Engineers (IEEE), Jan 1996.
The definitive version is available at https://doi.org/10.1109/ISAP.1996.501043
Meeting Name
International Conference on Intelligent Systems Applications to Power Systems, 1996
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Convergence Problems; Discriminant Analysis; Input Dimension Reduction; Learning (Artificial Intelligence); Neural Nets; Neural Network Training; Power System Analysis Computing; Power System Stability; Power System Transients; Power Systems; Training Algorithm; Transient Stability Assessment
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 1996 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jan 1996
