Date of Award
Summer 1992
Document Type
Thesis - Restricted
Degree Name
Master of Science (MS)
Department
Electrical and Computer Engineering
First Advisor
Feng, Xin
Second Advisor
Brown, Ronald H.
Third Advisor
Belfore, Lee A.
Abstract
Artificial Neural Network and Time Series Analysis are two emerging technologies. Neural Networks provide the potential for massive parallel computing architectures that have many computational advantages over traditional algorithm based methods. Studies of time series have shown that the Dynamic Data System method is a powerful tool for analyzing time series from a systematic point of view. This research is concerned with performing noisy dynamic signal identification by using a hybrid time series and neural network method. Simulated vibration signals are generated and time series anallysis methods are applied to estimate dynamic models for the observations, then the frequency decompositions of the signals are obtained from their estimated parameters. By feeding these sequences to back propagation neural networks, the frequency components are identified. The results demonstrate that the combined time series and neural network method is an effective tool for vibration signal identification in noisy environments. It also can be extended to other dynamic signal identification/classification applications. The hybrid time series and neural network method has the potential for building a connectionist (or neural network) expert system to identify noisy dynamic signals. This implementation has several advantages over traditional expert systems and is suitable for real world applications.
Recommended Citation
Schulteis, Jessica, "Feature Identification of Dynamic Signals Using a Hybrid Time Series and Neural Network Approach" (1992). Master's Theses (1922-2009) Access restricted to Marquette Campus. 4176.
https://epublications.marquette.edu/theses/4176