Prime-Fork Neural Network (PFNN): A Dynamic Neural Architecture Driven by Prime Number Transitions

Fuente: Zenodo

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Bibliographische Detailangaben
Hauptverfasser:	Rezapour, Majid, Rezapour, Ramin
Format:	Recurso digital
Sprache:	Englisch
Veröffentlicht:	Zenodo 2025
Schlagworte:	Prime Numbers Neural Architecture Function Switching Modular Logic Prime-Indexed Transition Dynamic Neural Network Adaptive Computation Nonlinear Activation Prime-Based Encoding Hybrid AI Models Discrete Learning Systems Mathematical Discontinuity Logic-Based Switching Forked Neural Logic Entropy-Guided Adaptation Phase-Switch Neural Dynamics Intelligent Signal Routing Deterministic Function Selection Gradient-Sensitive Architecture Prime-Fork Neural Framework
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