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Simulink 7.5

Product Description

Defining and Managing Signals and Parameters

Simulink® enables you to define and control the attributes of signals and parameters associated with your model. Signals are time-varying quantities represented by the lines connecting blocks. Parameters are coefficients that help define the dynamics and behavior of the system.

Signal and parameter attributes can be specified directly in the diagram or in a separate data dictionary. Using the Model Explorer, you can manage your data dictionary and quickly repurpose a model by incorporating different data sets.

You can define the following signal and parameter attributes:

  • Data type—single, double, signed or unsigned 8-, 16- or 32-bit integers; Boolean; and fixed-point
  • Dimensions—scalar, vector, matrix, or N-D arrays
  • Complexity—real or complex values
  • Minimum and maximum range, initial value, and engineering units
sl_modelexplorer

Model Explorer used to navigate, create, configure, and search all signals, parameters, properties, and generated code associated with your model. Click on image to see enlarged view.

Fixed-point data types provide support for scaling and arbitrary word lengths of up to 128 bits. These data types require Simulink® Fixed Point™ software (available separately) to simulate and generate code.

You can also specify the signal sampling mode as sample-based or frame-based, to enable the faster execution of signal processing applications in Simulink and Signal Processing Blockset™ (available separately).

Using Simulink data-type objects, you can define custom data types and bus signals. Bus signals let you define interfaces between design components.

Simulink lets you determine the level of signal specification. If you do not specify data attributes, Simulink determines them via propagation. You can specify only component interfaces or all data for your model. In all instances, Simulink conducts consistency checking to ensure data integrity.

You can restrict the scope of your parameters to specific parts of your model through a hierarchy of workspaces, or share them across models via a global workspace.

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