Identify, trace, and debug sources of overflow, precision loss, and wasted range or precision. Compare embedded implementation against ideal floating-point behavior.
Data type override using the
Examples of using
fipref objects to set logging preferences
This example shows how to compute and compare the statistics of the signal quantization error when using various rounding methods.
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.
Inspect and compare floating-point and fixed-point logged input and output data.
Visualize numerical differences during fixed-point conversion.
Detect overflows using the app.
Example showing how to use the Fixed-Point Tool to compare floating-point and fixed-point data types.
Control the warning messages you receive when a model contains an overflow.
Net slope and bias precision, detecting precision loss, underflow, and overflow.
This example shows how to detect fixed-point constant precision loss.
How to avoid precision loss by overriding the data types in your model with scaled doubles.