Activation-Induced Symmetric Kernels for Neural Network Approximation with Quantitative Error Analysis
The results indicate that symmetrization improves approximation accuracy, while parameter tuning remains necessary.
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The results indicate that symmetrization improves approximation accuracy, while parameter tuning remains necessary.
A Banach space-valued approximation framework based on symmetrized neural network (SNN) operators generated by a deformation-dependent sigmoidal activation function, yielding a positive, even, normalized, and localized density kernel satisfying the partition of unity.
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