How can i convert fis MATLAB fuzzy
MATLAB's Fuzzy Logic Toolbox stores fuzzy systems in .fis files. You may need to convert between different formats or versions for compatibility, sharing, or deployment.
| Goal | Method | Code/Example |
|---|---|---|
| Load old .fis file (any version) | readfis() – works with all legacy and current .fis files | fis = readfis('mycontroller.fis'); |
| Save current FIS to .fis file | writefis() (older) or savefis() (recommended) | savefis(fis, 'newcontroller'); |
| Convert between FIS types (e.g., Mamdani ↔ Sugeno) | Use convertfis() (R2020a+) or manual conversion | fis2 = convertToSugeno(fis1); fis2 = convertToType1(fis1); (for Type-2 → Type-1) |
| Export to FIS v2.0 format (most compatible) | Always save with current MATLAB version → automatically uses latest format | savefis(fis, 'myfis_v2'); |
| Import from text description (e.g., .fis text format) | readfis() works directly on text .fis files | fis = readfis('controller.txt'); |
| Convert FIS to code (C, Simulink, etc.) | genfis() + code generation tools or evalfis() for simulation | Use Fuzzy Logic Designer → Export → To Workspace then Simulink block |
| Deploy to standalone / embedded | Export as C code via MATLAB Coder or Simulink Coder | Requires MATLAB Coder + Embedded Coder |
| Open in newer MATLAB version | Just use readfis() — backward compatible since R2017a | No extra steps needed |
% Load any old .fis and resave as current version fis = readfis('old_controller.fis'); savefis(fis, 'modern_controller'); % creates modern_controller.fis % Convert Mamdani to Sugeno (zero-order or first-order) fis_sugeno = convertToSugeno(fis); % R2020a and laterfis = mamfis('Name','obstacle_avoidance'); fis = addInput(fis,[0 10],'Name','distance'); fis = addMF(fis,'distance','trimf',[0 2 5],'Name','close'); % ... then save savefis(fis,'obstacle_avoidance')“I got full marks on my MATLAB assignment! The solution was perfect and delivered well before the deadline. Highly recommended!”
“Quick delivery and excellent communication. The team really understood the problem and provided a great solution. Will use again.”
Explore how MATLAB Solutions has helped clients achieve their academic and research goals through practical, tailored assistance.
In today\\\'s rapidly advancing era of automation, robotics control systems are evolving to meet the demand for smarter, faster, and more reliable performance. Among the many innovations driving this transformation is the use of MCP (Model-based Control Paradigms)
The financial sector is witnessing a technological revolution with the rise of Large Language Models (LLMs). Traditionally used for text analysis, LLMs are now being integrated with powerful platforms like MATLAB to develop financial forecasting models