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Modeling and analysis of stochastic differential equations driven by point processes

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The modeling and analysis of nonlinear systems described by differential equations driven by point process noise are considered. The stochastic calculus of McShane is generalized to include such differential equations, and a more general canonical extension is defined. It is proved that this canonical extension possesses the same desirable properties for point process noise that it does for the noise processes, such as Brownian motion, considered by McShane. In addition, a new stochastic integral with respect to a point process is defined; this alternative integral obeys the rules of ordinary calculus. As a special case of the analysis of such systems, linear systems with multiplicative point process noise are investigated. The consistency of the canonical extension is studied by means of the product integral. Finally, moment equations and criteria for the stochastic stability of linear systems with multiplicative Poisson noise are derived.

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IEEE Transactions on Information Theory  (Volume:24 ,  Issue: 2 )