SmoothNN-th order exponential smooth with constant averaging time |
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A smooth is an exponential information delay. It is typically used to model time lags in forming an expectation or in perceiving some information. A smooth of order n
is modeled as a cascade of n
first-order exponential smooth components, where the output of a preceding component is the input for its successor. The delay time for each smooth in the cascade will be delayTime/n
.
delayTime
given (either as constant parameter or continuous signal) will effectively be clipped to a minimum value of n × modelSettings.dt
. The delay time for any first-order smooth in the cascade can thus never become smalle than dt
, which denotes the minimum process time anywhere in the model.init
in the Advanced tab allows to select →InitializationOptions:initialValue
to determine the initial output y.initialValue
as an initial guess.n |
Value: 3 Type: Integer Description: Order of the smooth |
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delayTime |
Value: 1 Type: Time (s) Description: Constant delay time (optional) |
initialValue |
Value: 0 Type: OutputType Description: Initial Output |
hasConstantDelayTime |
Value: true Type: Boolean Description: if true, the constant parameter value is used |
init |
Value: modelSettings.init Type: InitializationOptions Description: Provide InitializationOptions (Free, FixedValue, SteadyState) |
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u |
Type: RealInput Description: Input |
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y |
Type: RealOutput Description: Output signal |
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u_delayTime |
Type: RealInput Description: Delay time input |
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modelSettings |
Type: ModelSettings Description: Setting important global variables within a model |
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partialDelayTime |
Type: Division Description: Delay time for each smooth in the chain of smooths |
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firstOrderSmooth |
Type: Smooth[n] Description: Chain of n first-order exponential information delays |
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parN |
Type: ConstantConverter Description: Order of the delay |
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BusinessSimulation.MoleculesOfStructure.Blocks Modeling the influence of excessive workloads on productivity or quality |