Summary of Gradient-based Fuzzy System Optimisation Via Automatic Differentiation — Fuzzyr As a Use Case, by Chao Chen et al.
Gradient-based Fuzzy System Optimisation via Automatic Differentiation – FuzzyR as a Use Case
by Chao Chen, Christian Wagner, Jonathan M. Garibaldi
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the intersection of fuzzy systems and explainable AI, discussing the limitations of current fuzzy system design and proposing a gradient-descent-based optimization approach. The authors highlight the need to free designers from intricate derivative computations, allowing them to focus on functional and explainability aspects. They present a use case in FuzzyR, demonstrating how automatic differentiation tools can be leveraged to improve fuzzy inference systems. The paper’s main contribution is the integration of automatic differentiation with fuzzy system design, enabling more effective optimization and potentially transforming the field of fuzzy systems. Key phrases include gradient descent, automatic differentiation, fuzzy systems, explainable AI, and FuzzyR. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make fuzzy systems work better by using a special kind of math called “automatic differentiation”. Right now, designing fuzzy systems is hard because you need to do lots of complicated calculations. The authors want to change this by letting computers do those calculations for us, so we can focus on making the fuzzy systems more useful and easier to understand. They show how this works in a tool called FuzzyR and think it could be important for the future of fuzzy systems. |
Keywords
» Artificial intelligence » Gradient descent » Inference » Optimization
