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Lyapunov Stability Analysis and On-Line Monitoring
Point of Contact	Bojan Cukic cukic@csee.wvu.edu
Dates	May 2003 - April 2006
Problem	Artificial Neural Networks (ANN) play an increasingly important role in flight control and navigation, two focus areas for NASA. They are very useful in application domains that arise routinely within NASA's practice areas, where autonomy and adaptability are important features. A major obstacle, however, precludes the widespread use of ANN?s in navigation and control systems. Most of the certification standards that NASA and other federal agencies (such as FAA) impose on such life-critical and mission-critical applications cannot be met with today?s V&V technology. No existing software V&V method/ technique can be applied to systems, which contain on-line learning artificial neural networks.
Objective	The objective of this project is to fill the gap identified in the problem statement, by producing a framework for reasoning about adaptive systems. In the short term, this objective involves the following goals: Derive understanding of theself-stabilization analysis techniques suitable for neural network verification. Develop an analysis model and show its applicability for static system analysis and run-time monitoring. Investigate the applicability of the developed analysis method with respect to the verification /certification techniques currently developed by WVU/ISR/NASA IV&V. In the medium term, we intend to derive engineering techniques for the verification of adaptive systems, and to investigate how these techniques can be used to meet predefined certification standards.
Results	SAS 05 Executive Presentation.ppt Lyapunov Stability Analysis of the Quantization Error for DCS Neural Networks.pdf Self-stabilization of Online Neural Network in Adaptive Flight Control System.pdf SAS 05 Technical Presentation.ppt Global Asymptotic Lyapunov Analysis for Variable Input Manifolds.pdf Probabilistic extensions of Lyapunov analysis in unanticipated failure scenarios.pdf Operational size case studies, technology validation, tech transfer.pdf Verification and Validation Methodology based on Lyapunov on-line monitoring..pdf Lyapunov_FinalReport.pdf
Keywords	Artificial neural network, Multi-Layer Perception, Radial Basis Function, Dynamic Cell Structures, Intelligent Flight Control System
Categories	Software Reliability Software Safety Domain-Specific Analysis Formal Methods Test Analysis

Curator: Josh Stonestreet
NASA Official: Lisa Montgomery

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