This course is intended for hackers, hobbyists and professionals alike; Anyone that wants to get up-and-running quickly with a Fuzzy inference engine. If you have little or no knowledge of Fuzzy Logic, then this course is definitely for you!
At most you will require basic math skills and access to Google Sheets along with a willingness to use Fuzzy Logic to solve a problem.
I will walk you through the complete design process of a Fuzzy Controller or Inference system - From fuzzification to inference methods up to and including defuzzification.
This course will have you implementing your first Fuzzy System to solve your real world problem in a little more than half an hour.
This course is succinct yet comprehensive - It covers each aspect in enough detail to serve as a foundation but not so deep that you get bogged down in the details; it teaches you the lion's share...
Introduction to course including target student and prior knowledge. An overview on the course is also provided.
The big picture / block diagram of a typical Fuzzy system with an inference engine is discussed.
Fuzzification techniques are discussed; Different membership functions with their respective equations for calculating a degree of membership are introduced.
Provide the equations for calculating the degree of membership of the input to the membership functions.
Typical inferencing techniques used in Fuzzy Logic are discussed along with creating a fuzzy rule base.
A detailed inference example where the fuzzy output from several rules are calculated in order to aid understanding of Fuzzy inference principles.
Defuzzification techniques are introduced through which crisp output(s) are derived from the fuzzy output variables.
The design of the Fuzzy system implemented in Google Sheets is motivated at the hand of engineering/system trade-offs.
The implementation in Google Sheets is discussed including a line-by-line explanation of the code.
The worked examples provided in the spreadsheets of the previous lectures are discussed. This lecture is core to building the Fuzzy design understanding.
Concluding remarks and recommended practice.
Petrus Rohland obtained his bachelor's degree in electrical and electronic engineering in 2007. He is a Jack of all trades with an eclectic work history that spans a decade. Multi-disciplinary collaboration is a hallmark of his experiences which include research and development in the defense and maritime industries. His interests are as varied as his work experiences but computational intelligence and control systems are the most prominent contenders.