NDLI: Novelty detection for a neural network-based online adaptive system

Content Provider	IEEE Xplore Digital Library
Author	Liu, Y. Cukic, B. Fuller, E. Gururajan, Srikanth Yerramalla, S.
Copyright Year	2005
Description	Author affiliation: West Virginia Univ., Morgantown, WV, USA (Liu, Y.; Cukic, B.; Fuller, E.; Srikanth Gururajan; Yerramalla, S.)
Abstract	The appeal of including biologically inspired soft computing systems such as neural networks in complex computational systems is in their ability to cope with a changing environment. Unfortunately, continual changes induce uncertainty that limits the applicability of conventional verification and validation (V&V) techniques to assure the reliable performance of such systems. At the system input layer, novel data may cause unstable learning behavior, which may contribute to system failures. Thus, the changes at the input layer must be observed, diagnosed, accommodated and well understood prior to system deployment. Moreover, at the system output layer, the uncertainties/novelties existing in the neural network predictions also need to be well analyzed and detected during system operation. Our research tackles the novelty detection problem at both layers using two different methods. We use a statistical learning tool, support vector data description (SVDD), as a one-class classifier to examine the data entering the adaptive component and detect unforeseen patterns that may cause abrupt system functionality changes. At the output layer, we define a reliability-like measure, the validity index. The validity index reflects the degree of novelty associated with each output and thus can be used to perform system validity checks. Simulations demonstrate that both techniques effectively detect unusual events and provide validation inferences in a near-real time manner.
Sponsorship	IEEE Comput. Soc. Motorola
Starting Page	117
Ending Page	122
File Size	242767
Page Count	6
File Format	PDF
ISBN	0769524133
ISSN	07303157
DOI	10.1109/COMPSAC.2005.113
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-07-26
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Adaptive systems Aerospace control Biology computing Control systems Intelligent systems Intelligent control Computer networks Uncertainty Automatic control
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications Software

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