↑ arvau

Wikipedia / Discretization

In applied mathematics, discretization is the process of transferring continuous functions, models, variables, and equations into discrete counterparts. This process is usually carried out as a first step toward making them suitable for numerical evaluation and implementation on digital computers. Dichotomization is the special case of discretization in which the number of discrete classes is 2, which can approximate a continuous variable as a binary variable (creating a dichotomy for modeling purposes, as in binary classification).

Discretization is also related to discrete mathematics, and is an important component of granular computing. In this context, discretization may also refer to modification of variable or category granularity, as when multiple discrete variables are aggregated or multiple discrete categories fused.

Whenever continuous data is discretized, there is always some amount of discretization error. The goal is to reduce the amount to a level considered negligible for the modeling purposes at hand.

The terms discretization and quantization often have the same denotation but not always identical connotations. (Specifically, the two terms share a semantic field.) The same is true of discretization error and quantization error.

Mathematical methods relating to discretization include the Euler–Maruyama method and the zero-order hold.

Discretization of linear state space models discrete function

Discretization is also concerned with the transformation of continuous differential equations into discrete difference equations, suitable for numerical computing.

The following continuous-time state space model

where and are continuous zero-mean white noise sources with power spectral densities

can be discretized, assuming zero-order hold for the input and continuous integration for the noise , to

with covariances

where

and is the sample time. If is nonsingular

The equation for the discretized measurement noise is a consequence of the continuous measurement noise being defined with a power spectral density.

A clever trick to compute and in one step is by utilizing the following property:

Where and are the discretized state-space matrices.

Discretization of process noise

Numerical evaluation of is a bit trickier due to the matrix exponential integral. It can, however, be computed by first constructing a matrix, and computing the exponential of it The discretized process noise is then evaluated by multiplying the transpose of the lower-right partition of with the upper-right partition of

Derivation

we know that the matrix exponential which is an analytical solution to the continuous model.

Now we want to discretise the above expression. We assume that is constant. We also assume that is constant during the integral, which in turn yields

which is an exact solution to the discretization problem.

When by its Taylor expansion which is the form used in practice.

Approximations

This is also known as the Euler method, otherwise known as the backward Euler method, which is known as the bilinear transform, or Tustin transform. Each of these approximations has different stability properties. The bilinear transform preserves the instability of the continuous-time system.

Discretization of continuous features

In statistics and machine learning, discretization refers to the process of converting continuous features or variables to discretized or nominal features. This can be useful when creating probability mass functions.

Discretization of smooth functions

In generalized functions theory, discretization arises as a particular case of the Convolution Theorem on tempered distributions

is the Dirac comb is periodization is a rapidly decreasing tempered distribution (e.g. a Dirac delta function or any other compactly supported is a smooth, slowly growing ordinary function or any other band-limited is the (unitary, ordinary frequency) Fourier transform which are not smooth can be made smooth using a mollifier prior to discretization.

yields the sequence which, interpreted as the coefficients of a linear combination of Dirac delta functions, forms a Dirac comb. If additionally truncation. They are discrete in both, time and frequency.

See also

References

Further reading