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

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

doi:10.1109/TNNLS.2021.3123533

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

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

MED - Health Sciences

Research output: Contribution to journal › Article › peer-review

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 (IPLNAs) 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.

Original language	English
Journal	IEEE Transactions on Neural Networks and Learning Systems
DOIs	https://doi.org/10.1109/TNNLS.2021.3123533
Publication status	Accepted/In press - 2021

Keywords

Bounded-input bounded-state stability (BIBS)
Convergence
Kernel
Learning systems
Loss measurement
Mechanical engineering
Neural networks
Stability criteria
extreme learning machines
in-parameter-linear nonlinear neural architectures (IPLNAs)
incremental gradient learnings
input-to-state stability (ISS)
polynomial neural networks
random vector functional link networks
weight convergence.

ASJC Scopus subject areas

Software
Computer Science Applications
Computer Networks and Communications
Artificial Intelligence

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10.1109/TNNLS.2021.3123533

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title = "Letter on Convergence of In-Parameter-Linear Nonlinear Neural Architectures With Gradient Learnings",

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 (IPLNAs) 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.",

keywords = "Bounded-input bounded-state stability (BIBS), Convergence, Kernel, Learning systems, Loss measurement, Mechanical engineering, Neural networks, Stability criteria, extreme learning machines, in-parameter-linear nonlinear neural architectures (IPLNAs), incremental gradient learnings, input-to-state stability (ISS), polynomial neural networks, random vector functional link networks, weight convergence.",

author = "Ivo Bukovsky and Gejza Dohnal and Benes, {Peter M.} and Kei Ichiji and Noriyasu Homma",

note = "Publisher Copyright: IEEE",

year = "2021",

doi = "10.1109/TNNLS.2021.3123533",

language = "English",

journal = "IEEE Transactions on Neural Networks and Learning Systems",

issn = "2162-237X",

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T1 - Letter on Convergence of In-Parameter-Linear Nonlinear Neural Architectures With Gradient Learnings

AU - Bukovsky, Ivo

AU - Dohnal, Gejza

AU - Benes, Peter M.

AU - Ichiji, Kei

AU - Homma, Noriyasu

N1 - Publisher Copyright: IEEE

PY - 2021

Y1 - 2021

N2 - 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 (IPLNAs) 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.

AB - 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 (IPLNAs) 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.

KW - Bounded-input bounded-state stability (BIBS)

KW - Convergence

KW - Kernel

KW - Learning systems

KW - Loss measurement

KW - Mechanical engineering

KW - Neural networks

KW - Stability criteria

KW - extreme learning machines

KW - in-parameter-linear nonlinear neural architectures (IPLNAs)

KW - incremental gradient learnings

KW - input-to-state stability (ISS)

KW - polynomial neural networks

KW - random vector functional link networks

KW - weight convergence.

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DO - 10.1109/TNNLS.2021.3123533

M3 - Article

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JO - IEEE Transactions on Neural Networks and Learning Systems

JF - IEEE Transactions on Neural Networks and Learning Systems

SN - 2162-237X

ER -

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

Abstract

Keywords

ASJC Scopus subject areas

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