Share Email Print
cover

Proceedings Paper

Anomaly detection in data using neural networks with natural selection
Author(s): Patrick E. Dessert
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Frequently, time series data taken off machines contains erroneous data points due to errors in the measurement of the data. One such instance of measuring devices recording anomalies occurs in the `crash testing' of vehicles. In this task, senors are placed on the vehicle and the `crash dummy' and the vehicle is then crashed into a barrier. Force and acceleration data is collected which an engineer inspects for anomalies, correcting those that are found. Artificial neural network (ANN) technology was successfully applied to this problem to eliminate the cost and delay of this manual process. To apply ANN technology in this domain, two technical problems needed to be resolved; the appropriate network architecture and the size of the input set. These two issues are quite common and must be addressed in the development of any neural network application. To resolve both issues, I employed a machine learning algorithm that simulates the Darwinian concept of `survival of the fittest' known as the genetic learning algorithm (GLA). By combining the strength of the GLA and ANNs, a network architecture was created that `optimized' the size, speed, and accuracy of the ANN. This `hybridized' system also used the GLA to determine the `smallest' number of inputs into the ANN that were necessary to detect anomalies in data. This algorithm is known as GENENET, and is described in this paper.

Paper Details

Date Published: 1 July 1992
PDF: 9 pages
Proc. SPIE 1710, Science of Artificial Neural Networks, (1 July 1992); doi: 10.1117/12.140133
Show Author Affiliations
Patrick E. Dessert, Carnegie Group, Inc. (United States)


Published in SPIE Proceedings Vol. 1710:
Science of Artificial Neural Networks
Dennis W. Ruck, Editor(s)

© SPIE. Terms of Use
Back to Top