This series introduces fuzzy logic and fuzzy inference systems (FIS). With Boolean logic, a statement can only have two truth values: true or false. With fuzzy logic, the truth of a statement can have any value that ranges between 0 (absolutely false) and 1 (absolutely true). Learn how to use fuzzy logic to design a FIS, which is a function that maps a set of inputs to outputs using human-interpretable rules rather than more abstract mathematics. Developing a FIS doesn’t require a model, so it works well for complex systems with underlying mechanisms that are not fully known. If you have some experience and intuition about the system, then you can develop and implement the rules.
Part 1: What Is Fuzzy Logic? This video introduces fuzzy logic and explains how you can use it to design a fuzzy inference system (FIS), which is a powerful way to use human experience to design complex systems.
Part 2: Fuzzy Inference System Walkthrough This video walks step-by-step through a fuzzy inference system. Learn concepts like membership function shapes, fuzzy operators, multiple-input inference systems, and rule firing strength.
Part 3: Fuzzy Logic Examples Watch this fuzzy logic example of a fuzzy inference system that can balance a pole on a cart. You can design a fuzzy logic controller using just experience and intuition about the system—no mathematical models necessary.
Part 4: Fuzzy Logic Controller Tuning This video covers the basics of data-driven approaches to tuning fuzzy inference systems. Follow along with an example about tuning a fuzzy inference system using data that controls an artificial pancreas.