Modeling continuous noise sources in digital simulations

Article Abstract:

Physical systems often contain subsystems that are "best" modeled as continuous random processes. When these processes are included in digital simulations, the statistics of the random number generator used in the simulation must be selected such that the continuous random process is modeled faithfully. In general the statistics of the random number generator will be drastically different from the statistics of the continuous random process. This paper presents the functional relationship between the statistics of the random number generator and the statistics of the continuous random process for simulations using two commonly employed integration methods. (Reprinted by permission of the publisher.)

Statistics (Data), Noise (Sound), Simulation, Stochastic Model, Statistics, Statistical Analysis, Random Number Generation, Noise, Simulation of Computer Systems

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Modeling noncoventional sampling schemes with a combined discrete-continuous language

Article Abstract:

Combined discrete-continuous simulation languages can provide a convenient and flexible structure for modeling a wide variety of nonconventional sampled-data control problems. Using the language SIMAN, it is shown how to model fixed, random, and signal-dependent nonconventional sampling schemes including a nonsynchronous multirate system with a PID controller. (Reprinted by permission of the publisher.)

Control systems, Modeling, Data modeling software, Simulation Languages, Discrete Simulation, Controllers, Sampling

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