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Balanced Neurofuzzy Models

Balanced Neurofuzzy Models. Mytnyk Oleg. Structure Identification Cyclic Process. Interpretation. Validation. Design. Learning. Interpretation depends on transparency. Transparent model → easy interpretation Opaque models are hardly interpretable

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Balanced Neurofuzzy Models

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  1. Balanced Neurofuzzy Models Mytnyk Oleg

  2. Structure Identification Cyclic Process Interpretation Validation Design Learning

  3. Interpretation depends on transparency • Transparent model → easy interpretation • Opaque models are hardly interpretable • Are we able to interpret the following recurrent neural network? To understand relations?

  4. Fuzzy Logic – Step to Transparency • ANFIS (Adaptive Network Based Fuzzy Inference System) • Drawbacks: • High number of rules: • Each rule is compound: and…and…and If (x1 is A1i1) and (x2 is A2i2) and … and (xn is Anin) then y = fj(x1, x2, … xn) O(mn) ik = 1…m

  5. Main Result for NeuroFuzzy Models • Brown M. (1994) – Neurofuzzy model • Main result - fuzzy membership for fuzzy label - confidence of the rule

  6. NeuroFuzzy Models Decomposition • Harris C.J. (2000) - Decomposition of multi-dimensional neurofuzzy model into submodels using Gabor-Kolmogorov expansion. • Rules are generated separately for each submodel • Number of rules O(n)+… • But independent use of submodels is not proved

  7. Balanced NeuroFuzzy Models • Balanced neurofuzzy model • Rules are generated separately for each submodel • Independent use is grounded on maximum entropy principle

  8. Example • Problem – predict wave height based on wind direction and speed. • 6 easy rules is set if wind direction is West then waves are average (0.76) or big (0.24) if wind direction is East then waves are small (0.48) or average (0.52) if wind direction is South then waves are small (0.28) or average (0.72) if wind direction is North then waves are average (0.96) or big (0.04) if wind is weak then calm (0.52) or waves are small (0.48) if wind is strong then waves are big (0.72) or storm (0.28)

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