What question did this study set out to answer?

To demonstrate that binary divergence causes errors in neural networks and propose a ternary logic framework as a solution.

April 14, 20260 citationsOpen Access

View Full Paper

Convergence Proof of Ternary Logic Manifold in Non-Stationary Computing and the Elimination of Binary Divergence

DWDa Wei

Key Points

To demonstrate that binary divergence causes errors in neural networks and propose a ternary logic framework as a solution.
Developed a mathematical proof for the Ternary Logic Manifold (M3).
Analyzed systemic errors in neural network backpropagation due to binary logic.
Presented foundational axioms that eliminate the need for empirical weight adjustments.
Proved that binary divergence significantly contributes to errors in neural networks.
Established that the M3 achieves absolute convergence in high-dimensional computations.
Demonstrated the natural absorption state for tangential force release.

Abstract

Traditional computational architectures rely on Boolean binary logic, which intrinsicallyforces topological truncation when processing non-stationary, high-dimensional manifolds.This paper mathematically proves that binary divergence is the root cause of systemic errorsin current neural network backpropagation and physical simulations. By introducing theTernary Logic Manifold (M3), we establish a natural absorption state for tangential forcerelease, achieving absolute convergence without relying on empirical weight adjustments. Noexternal references are cited, as the foundational axioms presented herein supersede priorbinary frameworks.

AI से पूछें

Bookmark

View Full Paper

AI से पूछें

Bookmark

View Full Paper

Convergence Proof of Ternary Logic Manifold in Non-Stationary Computing and the Elimination of Binary Divergence

Key Points

Abstract

Cite This Study