Summary: System Identification And Parameter Estimation

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• L1 Introduction: signals, systems and models

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• What is system ID and where is it good for?

  System identification is building a mathematical model of a dynamic system from measured input-output data.
  It is useful for prediction and control of a system and for studying the mechanism of a system.

• What is a deterministic signal?

  A signal for which the future signal values are obtained from known equations, like a x(t) = sin(omega*t)

• What is a stationary signal?

  A signal that has a PDF which is independent of time: f(x,t)=f(x)

• What is an ergodic signal?

  A signal of which ensemble and time averages are equal. Ensemble =   verzameling van multiple realizations of a stochastic process.

• What is the probability density function of a signal?

  The PDF of a stochastic signal/process describes the signal/process.

• What is a static system?

  A system of which the output at one time instant depends on only the input at the same time instant.

• What is a dynamic system?

  A system where the output depends on some or all of the input history. Example: a time delay y(t) = u(t-tau)

• What is a nonlinear system?

  A system that does not obey both the scaling and superposition property.

• What is a time-variant system?

  A system of which its behavior depends on the passage of time. For example, muscles behave differently over time because of fatigue.

• What is the difference between an anti-causal system and a noncausal system?

  Noncausal system depends both on previous and future inputs, whereas an anti-causal system depends only on future inputs.