Open Access. Powered by Scholars. Published by Universities.®
Electrical and Computer Engineering
Electrical and Computer Engineering Faculty Research & Creative Works
Articles 1 - 2 of 2
Full-Text Articles in Engineering
Input Dimension Reduction In Neural Network Training-Case Study In Transient Stability Assessment Of Large Systems, S. Muknahallipatna, Badrul H. Chowdhury
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 Faculty Research & Creative Works
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.
Two Methods Of Neural Network Controlled Dynamic Channel Allocation For Mobile Radio Systems, Kelvin T. Erickson, Edward J. Wilmes
Two Methods Of Neural Network Controlled Dynamic Channel Allocation For Mobile Radio Systems, Kelvin T. Erickson, Edward J. Wilmes
Electrical and Computer Engineering Faculty Research & Creative Works
Two methods of dynamic channel allocation using neural networks are investigated. Both methods continuously optimize the mobile network based on changes in calling traffic. The first method uses backpropagation model predictions to aid the channel allocator. Each cell contains a backpropagation model which provides the channel allocator a call traffic prediction allowing the channel allocator to effectively optimize the network. The second method uses the same backpropagation models along with actor-critic models to perform the channel allocation. The actor-critics learn to model traffic activity between adjacent cells in real-time, and thereby learn to allocate channels dynamically between cells. The learning …