Overflow and Precision Loss Detection
Debug sources of overflow and precision loss, compare to floating-point behavior
Identify, trace, and debug sources of overflow, precision loss, and wasted range or precision. Compare embedded implementation against ideal floating-point behavior.
|Parameter Quantization Advisor||Inspect numerical issues related to parameter quantization|
- Data Type Override Preferences Using fipref
Data type override using the
- Underflow and Overflow Logging Using fipref
Examples of using
fiprefobjects to set logging preferences for
- Compute Quantization Error
This example shows how to compute and compare the statistics of the signal quantization error when using various rounding methods.
- Visualize Differences Between Floating-Point and Fixed-Point Results
Use a custom plot function to compare the behavior of the generated fixed-point code against the behavior of the original floating-point MATLAB code.
- Enable Plotting Using the Simulation Data Inspector
Inspect and compare floating-point and fixed-point logged input and output data.
- Custom Plot Functions
Visualize numerical differences during fixed-point conversion.
- Detect Overflows
Detect overflows using the app.
- Use the Fixed-Point Tool to Explore Numerical Behavior
Example showing how to use the Fixed-Point Tool to compare floating-point and fixed-point data types.
- Handle Overflows in Simulink Models
Control the warning messages you receive when a model contains an overflow.
- Net Slope and Net Bias Precision
Net slope and bias precision, detecting precision loss, underflow, and overflow.
- Detect Fixed-Point Constant Precision Loss
This example shows how to detect fixed-point constant precision loss.
- Use Scaled Doubles to Avoid Precision Loss
How to avoid precision loss by overriding the data types in your model with scaled doubles.