Neural Networks Applied to Electromagnetic Compatibility (EMC) Simulations

Author

Huseyin Goksu
Donald C. Wunsch, Missouri University of Science and TechnologyFollow

Abstract

Data extrapolation in FDTD simulations using feedforward multi-layer Perceptron (MLP) showed promising results in a previous study. This work studies two different aspects of the problem: First is the learning aspect, including the effect of prior training with the same class of random signals, which is an attempt to find a general solution to the weight initialization problem in adaptive systems. The second aspect covers the steps to make the extrapolator fully adaptive, through optimization of the time step sensitivity and the input layer width of a sliding window extrapolator. Average mutual information is used as a performance measure in most of the work.

Recommended Citation

H. Goksu and D. C. Wunsch, "Neural Networks Applied to Electromagnetic Compatibility (EMC) Simulations," Lecture Notes in Computer Science, Springer, Jun 2003.

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

Adaptive Method; Electromagnetic; Multilayer Network; Multilayer Perceptrons; Neural Network; Optimization; Probabilistic Approach; Random Signal; Time Domain Method

International Standard Serial Number (ISSN)

0302-9743

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2003 Springer, All rights reserved.

Publication Date

01 Jun 2003