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We called this model a Discrete Cosine Model (DCM).
To call such a model a discrete geometrical model seems quite inappropriate.
A transform method was used to model a discrete time multi-tap direct sampling mixer.
Next to the stochastic model, a discrete and a continuous deterministic model (conf. Sections 3.2.1 and 3.2.3) are investigated.
The paper presents an original approach to model a discrete manufacturing system by Parts of Plant (PoP).
They applied this distribution to model a discrete data se related to accidents of 647 women working on Shells for 5 weeks.
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For the obtained neural model, a discrete-time sliding mode control law is designed for trajectory tracking of velocity and flux magnitude.
We considered the equilibrium of this model (a discrete-time system) and provided a theorem for its stability, the appropriate control actions and the total system design in order to obtain a stable situation.
In addition, we consider the equilibrium(s) of the model (a discrete-time system) and study properties for stability, the appropriate control actions as well as the total system design in order to obtain a stable situation.
A recurrent high order neural network structure is used to design a nonlinear observer and based on this model, a discrete-time control law is derived, which combines discrete-time block control and sliding modes techniques.
Then in the basis of this model, a discrete-time sliding mode regulator is designed in order to force the boost circuit to track a DC-biased sinusoidal signal.
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