xFODE uses incremental states and fuzzy additive models trained with partitioning strategies to deliver accurate yet interpretable nonlinear dynamic models that match NODE and FODE performance on benchmarks.
Gami-net: An explainable neural network based on generalized additive models with structured interactions,
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
xFODE+ is an interpretable type-2 fuzzy additive ODE model for system identification that produces prediction intervals with point predictions and retains physically meaningful states.
citing papers explorer
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xFODE: An Explainable Fuzzy Additive ODE Framework for System Identification
xFODE uses incremental states and fuzzy additive models trained with partitioning strategies to deliver accurate yet interpretable nonlinear dynamic models that match NODE and FODE performance on benchmarks.
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xFODE+: Explainable Type-2 Fuzzy Additive ODEs for Uncertainty Quantification
xFODE+ is an interpretable type-2 fuzzy additive ODE model for system identification that produces prediction intervals with point predictions and retains physically meaningful states.