Potentials of Gated Higher Order Neural Units for Signal Decomposition and Process Monitoring

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F63839172%3A_____%2F25%3A10133656" target="_blank" >RIV/63839172:_____/25:10133656 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21220/25:00383706 RIV/60076658:12310/25:43911223

Result on the web

<a href="https://doi.org/10.1016/j.procs.2025.01.288" target="_blank" >https://doi.org/10.1016/j.procs.2025.01.288</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.procs.2025.01.288" target="_blank" >10.1016/j.procs.2025.01.288</a>

Result language

angličtina

Original language name

Potentials of Gated Higher Order Neural Units for Signal Decomposition and Process Monitoring

Original language description

In this paper, we propose an innovative shallow network architecture with gated higher-order neural units (gHONUs) for machine learning-based signal decomposition, which has the potential to improve anomaly detection and explainability while monitoring industrial processes as a real-time learning predictor. Our study shows that native HONU networks have limited decomposition capabilities due to the lack of a mechanism that forces the optimizer to enforce differential learning behavior among neurons. To address this limitation, we introduce pairs of neurons of different orders, connected by extended activation functions, as individual decomposition blocks. We present a first analysis of the behavior of these activation functions under HONU augmentations and show that the lowest linear correlation between paired neurons is achieved by a Tanh connection, where the neuron with an identity function output has a higher learning rate than the tanh signal constraining neuron. The possibilities of such a neural architecture are in applications of predictive and prescriptive maintenance and in analysis of time series resulting from complex dynamical behavior with incomplete observations and otherwise limited training, because the in-parameter linearity of nonlinear HONUs provides us with a mathematical-physical insight into the neural architecture.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/LM2023054" target="_blank" >LM2023054: e-Infrastructure CZ</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Procedia Computer Science

ISBN

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ISSN

1877-0509

e-ISSN

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Number of pages

10

Pages from-to

2278-2287

Publisher name

Elsevier B.V.

Place of publication

Praha

Event location

Praha

Event date

Nov 13, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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