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.
Functions
fixed.unifiedErrorCalculator.absoluteError | Absolute error of two numeric inputs (Since R2023b) |
fixed.unifiedErrorCalculator.bitsOfError | Bits of error of two numeric inputs (Since R2023b) |
fixed.unifiedErrorCalculator.relativeAbsoluteError | Relative absolute error of two numeric inputs (Since R2023b) |
Topics
MATLAB
- Data Type Override Preferences Using fipref
Data type override using thefipref
object. - Underflow and Overflow Logging Using fipref
Examples of usingfipref
objects to set logging preferences forfi
objects. - 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.
Simulink
- 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.