Input Dimension Reduction in Neural Network Training-Case Study in Transient Stability Assessment of Large Systems

S. Muknahallipatna; Badrul H. Chowdhury

doi:10.1109/ISAP.1996.501043

Input Dimension Reduction in Neural Network Training-Case Study in Transient Stability Assessment of Large Systems

S. Muknahallipatna
, Badrul H. Chowdhury

Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

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.

Original language	American English
Journal	Proceedings of the International Conference on Intelligent Systems Applications to Power Systems, 1996
DOIs	https://doi.org/10.1109/ISAP.1996.501043
State	Published - Jan 1 1996

Keywords

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

Disciplines

Electrical and Computer Engineering

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Input Dimension Reduction in Neural Network Training-Case Study iFinal published version, 651 KBLicense: Unspecified

Cite this

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title = "Input Dimension Reduction in Neural Network Training-Case Study in Transient Stability Assessment of Large Systems",

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.",

keywords = "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",

author = "S. Muknahallipatna and Chowdhury, \{Badrul H.\}",

year = "1996",

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doi = "10.1109/ISAP.1996.501043",

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AU - Chowdhury, Badrul H.

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N2 - 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.

AB - 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.

KW - Convergence Problems

KW - Discriminant Analysis

KW - Input Dimension Reduction

KW - Learning (Artificial Intelligence)

KW - Neural Nets

KW - Neural Network Training

KW - Power System Analysis Computing

KW - Power System Stability

KW - Power System Transients

KW - Power Systems

KW - Training Algorithm

KW - Transient Stability Assessment

UR - https://scholarsmine.mst.edu/ele_comeng_facwork/1682

U2 - 10.1109/ISAP.1996.501043

DO - 10.1109/ISAP.1996.501043

M3 - Article

JO - Proceedings of the International Conference on Intelligent Systems Applications to Power Systems, 1996

JF - Proceedings of the International Conference on Intelligent Systems Applications to Power Systems, 1996

ER -

Input Dimension Reduction in Neural Network Training-Case Study in Transient Stability Assessment of Large Systems

Abstract

Keywords

Disciplines

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