Adaptive Neuro-Fuzzy Inference systems (ANFIS) are an alternative solution to classical statistical methods such as regression, offering much greater capabilities. They provide a means to formulate and solve problems within a mathematical framework. The domains where these systems are used today are so extensive that listing them all is challenging. However, notable areas include engineering, biology, medicine, energy, water, natural disasters, and nutrition sciences. The technique presented in this educational collection is arguably one of the most well-known neuro-fuzzy systems. Articles related to this technique can even be found in prestigious publications like Nature and Science. These approaches are based on a combination of artificial neural networks and fuzzy systems. Therefore, in a ANFIS, the learning capability of neural networks and the inference capability of fuzzy systems are utilized together to create models with enhanced capabilities. After completing this course, you will be able to implement and write high-quality articles based on the presented technique for your own problem.

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  • 3 hours and 18 minutes of instructional videos
  • Programming and implementation tutorials
  • Integration with optimization algorithms tutorial
  • Practical training on ANFIS technique
  • Mathematical relationship extraction
  • Tips for writing articles
  • Access to executable codes


  • Part 1: Introduction, Computational Details, and Applications
  • Part 2: Implementation in MATLAB 2020
  • Part 3: How to Write a Paper Using the ANFIS Approach
  • Part 4: Extracting the Mathematical Framework and Presenting it in Scientific Articles
  • Part 5: ANFIS Modeling Based on the Transit Search Optimization Algorithm (2022)


Dr. Masoomeh Mirrashid
Research Fellow (Postdoctoral)

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