Andy Bartlett, MathWorks
You can model your algorithm in Simulink® using the default double data types for signals and the computations to simulate the ideal numerical behavior. However, when you use embedded data types in your Simulink model, you can encounter certain numerical precision issues because of the quantization error of the chosen data type, either fixed-point or single-precision floating point. Learn how you can leverage various diagnostics and suppression mechanisms to filter out the real precision loss and quantization error issues in your system under design.
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