Designed and trained fuzzy logic systems: an interpretable machine learning tool

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• Samuel Morillas Gómez, Universidad Politécnica de Valencia
• Thursday 18 November 2021, 14:00-15:00
• SS03.

If you have a question about this talk, please contact am2806.

The talk can also be attended via zoom. Join Zoom Meeting https://cl-cam-ac-uk.zoom.us/j/93170847589?pwd=dnhXV2JOMm5obm9md2tVTUpyZUlCZz09 Meeting ID: 931 7084 7589 Passcode: I4ATtPDo

Fuzzy theory is an old paradigm that has historically been applied in many fields of science and engineering. It provides a simple framework to implement systems able to codify imprecise human knowledge in terms of implication rules. This allows for human knowledge to be easily transformed into a workable algorithm. Lately, this approach lost interest in the big data era when we have plenty of data but sometimes lack of knowledge, which is key to design fuzzy logic based systems. However, recently, the approach to build fuzzy logic systems is changing: instead of creating them from human knowledge, big amounts of data are used to train the system, that is: to find the implication rules that link inputs and outputs and tune their parameters. In this talk, we will review the basics of fuzzy logic and provide some insights about how fuzzy logic systems can be learned from data. Also, an application of a trained fuzzy logic system for spectral curve recovery will be presented.

This talk is part of the Rainbow Group Seminars series.

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