Fuzzy Logic Designer
Use the Fuzzy Logic Designer app or command-line functions to interactively design and simulate fuzzy inference systems. Define input and output variables and membership functions. Specify fuzzy if-then rules. Evaluate your fuzzy inference system across multiple input combinations.
Fuzzy Inference Systems (FIS)
Implement Mamdani and Sugeno fuzzy inference systems. Convert from a Mamdani system to a Sugeno system or vice versa, to create and compare multiple designs. Additionally, implement complex fuzzy inference systems as a collection of smaller interconnected fuzzy systems using fuzzy trees.
Fuzzy Inference System Tuning
Tune membership function parameters and rules of a single fuzzy inference system or of a fuzzy tree using genetic algorithms, particle swarm optimization, and other Global Optimization Toolbox tuning methods. Train Sugeno fuzzy inference systems using neuro-adaptive learning techniques similar to those used for training neural networks.
Fuzzy Logic Deployment
Implement your fuzzy inference system in Simulink and generate C/C++ code or IEC61131-3 Structured Text using Simulink Coder™ or Simulink PLC Coder™, respectively. Use MATLAB Coder™ to generate C/C++ code from fuzzy inference systems implemented in MATLAB. Alternatively, compile your fuzzy inference system as a standalone application using MATLAB Compiler™.
Fuzzy Logic for Explainable AI
Use fuzzy inference systems as support systems to explain the input-output relationships modeled by an AI-based black-box system. Interpret the decision-making process of a black-box model using the explainable rule base of your fuzzy inference system.