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arXiv · 2111.12877

A Letter on Convergence of In-Parameter-Linear Nonlinear Neural Architectures with Gradient Learnings

Ivo Bukovsky·Gejza Dohnal·Peter M. Benes·Kei Ichiji·Noriyasu Homma
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Abstract

This letter summarizes and proves the concept of bounded-input bounded-state (BIBS) stability for weight convergence of a broad family of in-parameter-linear nonlinear neural architectures as it generally applies to a broad family of incremental gradient learning algorithms. A practical BIBS convergence condition results from the derived proofs for every individual learning point or batches for real-time applications.

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Ivo Bukovsky, Gejza Dohnal, Peter M. Benes, Kei Ichiji, Noriyasu Homma. 2021-11-25. A Letter on Convergence of In-Parameter-Linear Nonlinear Neural Architectures with Gradient Learnings. https://doi.org/10.1109/tnnls.2021.3123533

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