Ph.D Thesis | |
Ph.D Student | Saraf Yaakov |
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Subject | Info-Gap Detection of Anomalies |
Department | Department of Mechanical Engineering |
Supervisor | PROFESSOR EMERITUS Yakov Ben-Haim |
We present a new optimal approach for vector updating under severe conditions of uncertainty, where the uncertainty is described by info-gap models. This approach is applied both to vector estimation in static systems and to anomaly detection in dynamic systems. Probability-based algorithms, either for learning, estimation or hypotheses testing, assume a structure of a probability density function. The validity of such an assumption becomes questionable when we encounter a severe lack of information on the system model and the accompanying uncertainties, with very few measurements available. Info-gap models, which organize events into a family of nested sets of events without employing distribution functions, seem appealing as a tool for formulating learning and hypothesis test algorithms under such circumstances. The three main topics of the thesis are: