  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.
