KA-FCM uses B-spline functions on FCM edges, inspired by the Kolmogorov-Arnold theorem, to enable arbitrary non-monotonic causal modeling and outperforms standard FCM while matching MLPs on non-monotonic inference, symbolic regression, and chaotic forecasting tasks.
de Boor,A Practical Guide to Splines
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Non-monotonic causal discovery with Kolmogorov-Arnold Fuzzy Cognitive Maps
KA-FCM uses B-spline functions on FCM edges, inspired by the Kolmogorov-Arnold theorem, to enable arbitrary non-monotonic causal modeling and outperforms standard FCM while matching MLPs on non-monotonic inference, symbolic regression, and chaotic forecasting tasks.