Neural networks, while effective approximators within a dataset, struggle with extrapolation. ReLU networks exhibit linear behavior far from the dataset, making them unsuitable for time series extrapolation. Sigmoid or tanh-based networks behave like constant functions away from 0, while sine-based activation functions show promise for modeling periodic behavior, as demonstrated with various examples and functions.
Neural Networks For Periodic Functions: Redefining AI Applications
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