Use the function fixpt_look1_func_plot to find the maximum absolute error for the simple lookup table whose breakpoints are 0, 0.25, and 1. The corresponding Y data points of the lookup
Sample fixed-point implementations of a discrete lead filter and a discrete lag filter along with reference implementations in floating point.
This model shows how to exercise a custom C language S-function written to compute a fixed-point "product and sum" operation. To see the source code for the S-function, use the right-click
Find the approximation to an ideal function of y = sin(2*pi*x) over an input range [xmin,xmax] using a lookup table approach. Fixed-point applications often need to approximate a function
Implement a direct form filter in fixed point using fundamental building blocks such as Gain, Delay, and Sum.
A custom C language S-function written to generate a constant value. This operation is available in Simulink® with the "Constant" block, which can be used for comparison with this
Construct a fixed-point series cascade form filter using the fundamental building blocks of delay, sum, and gain.
Implement a parallel form filter in fixed point using fundamental building blocks such as Gain, Delay, and Sum.
This model shows how to propagate fixed-point data types in fixed-point S-Functions. It exercises a custom C language S-function written to enforce data types across multiple signals.
A custom C language S-function written to perform an arithmetic shift. This operation is available in Simulink® with the "Shift Arithmetic" block, which can be used for comparison with this
This model shows bit-true implementations of fixed-point direct type I and II filters with time-varying and time-invariant coefficients. These filters use the fundamental capabilities
Some of the features of Prelookup and Interpolation Using Prelookup blocks.
How Prelookup and Interpolation blocks share their parameter data in generated code.
Several ways to use S-functions to probe signal properties. Use this model for simulation only; it does not support code generation.
When you want to optimize for both memory and absolute tolerance, it is helpful to visualize the tradeoffs between the two. This example creates a lookup table approximation of the function
Optimize an existing Lookup Table block for memory efficiency. Open the model containing the Lookup Table block that you want to optimize.
Generate a memory-efficient lookup table that approximates the sin function. Define the approximation problem by creating a FunctionApproximation.Problem object.
Convert a neural network regression model in Simulink to fixed point using the Fixed-Point Tool and Lookup Table Optimizer and generate C code using Simulink Coder.