Fuzzy Logic Toolbox 2.2.11

Introduction

The Fuzzy Logic Toolbox extends the MATLAB technical computing environment with tools for the design of systems based on fuzzy logic. Graphical user interfaces (GUIs) guide you through the steps of fuzzy inference system design. Functions are provided for many common fuzzy logic methods, including fuzzy clustering and adaptive neurofuzzy learning.

The toolbox lets you model complex system behaviors using simple logic rules and then implement these rules in a fuzzy inference system. You can use the toolbox as a stand-alone fuzzy inference engine. Alternatively, you can use fuzzy inference blocks in Simulink and simulate the fuzzy systems within a comprehensive model of the entire dynamic system.

Like all MATLAB toolboxes, the Fuzzy Logic Toolbox can be customized. You can inspect algorithms, modify source code, and add your own membership functions or defuzzification techniques.

Key Features

• Specialized GUIs for building fuzzy inference systems and viewing and analyzing results
• Membership functions for creating fuzzy inference systems
• Support for AND, OR, and NOT logic in user-defined rules
• Standard Mamdani and Sugeno-type fuzzy inference systems
• Automated membership function shaping through neuroadaptive and fuzzy clustering learning techniques
• Ability to embed a fuzzy inference system in a Simulink model
• Ability to generate embeddable C code or stand-alone executable fuzzy inference engines

Balancing a pole on a moving cart. The system, which is similar to an inverted pendulum, uses a Fuzzy Controller block within Simulink to balance the pole.