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Dichotomization : ウィキペディア英語版
Discretization

In mathematics, discretization concerns the process of transferring continuous functions, models, 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. Processing on a digital computer requires another process called quantization. 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).
* Euler–Maruyama method
* Zero-order hold
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.
== Discretization of linear state space models ==
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
:\dot(t) + \mathbf B \mathbf(t) + \mathbf(t)
:\mathbf(t) = \mathbf C \mathbf(t) + \mathbf D \mathbf(t) + \mathbf(t)
where ''v'' and ''w'' are continuous zero-mean white noise sources with covariances
:\mathbf(t) \sim N(0,\mathbf Q)
:\mathbf(t) \sim N(0,\mathbf R)
can be discretized, assuming zero-order hold for the input ''u'' and continuous integration for the noise ''v'', to
:\mathbf() = \mathbf A_d \mathbf() + \mathbf B_d \mathbf() + \mathbf()
:\mathbf() = \mathbf C_d \mathbf() + \mathbf D_d \mathbf() + \mathbf()
with covariances
:\mathbf() \sim N(0,\mathbf Q_d)
:\mathbf() \sim N(0,\mathbf R_d)
where
:\mathbf A_d = e^ = \mathcal^\_
:\mathbf B_d = \left( \int_^e^d\tau \right) \mathbf B = \mathbf A^(\mathbf A_d - I)\mathbf B , if \mathbf A is nonsingular
:\mathbf C_d = \mathbf C
:\mathbf D_d = \mathbf D
:\mathbf Q_d = \int_^ e^ \mathbf Q e^ d\tau
:\mathbf R_d = \frac \mathbf R
and T is the sample time, although \mathbf A^T is the transposed matrix of \mathbf A.
A clever trick to compute ''A''''d'' and ''B''''d'' in one step is by utilizing the following property:〔Raymond DeCarlo: ''Linear Systems: A State Variable Approach with Numerical Implementation'', Prentice Hall, NJ, 1989〕
:e^ & \mathbf \\
\mathbf & \mathbf \end T} = \begin \mathbf} \\
\mathbf & \mathbf \end
and then having
:\mathbf A_d = \mathbf M_
:\mathbf B_d = \mathbf M_

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
ウィキペディアで「Discretization」の詳細全文を読む



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