Topology Optimization of Neural Networks as an Integrated Process in Training with Control Theory Methods

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F25%3A39923078" target="_blank" >RIV/00216275:25530/25:39923078 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/11047318" target="_blank" >https://ieeexplore.ieee.org/document/11047318</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/PC65047.2025.11047318" target="_blank" >10.1109/PC65047.2025.11047318</a>

Result language

angličtina

Original language name

Topology Optimization of Neural Networks as an Integrated Process in Training with Control Theory Methods

Original language description

The simultaneous optimization of neural network topology and training remains an underexplored research direction, despite its potential to improve model efficiency and performance dynamically. This paper introduces a control-based framework for jointly adjusting the structure and training process of fully connected neural networks. The methodology formulates the training and pruning process as a multivariable dynamic system with two input variables-training process parameters and network architecture adjustments-and two output variables-model performance and computational complexity. A discrete two-dimensional Proportional-Integral-Derivative (PID) controller is employed to regulate these inputs, ensuring a balanced trade-off between accuracy and computational efficiency. The control system is tested on a function approximation task, where a fully connected network is initially set with redundant capacity and gradually optimized according to predefined reference trajectories of performance and complexity. Experimental results demonstrate the effectiveness of the proposed approach, revealing the dynamic interaction between topology and training in realtime network adaptation. The findings highlight the feasibility of integrating control strategies into neural network optimization and pave the way for future research on more advanced control-based learning architectures.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

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/EH23_021%2F0008402" target="_blank" >EH23_021/0008402: Multi-sector and Interdisciplinary Cooperation in Research and Development of Communication, Information and Detection Technologies for Control and Signalling Systems (CIDET)</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

2025 25TH INTERNATIONAL CONFERENCE ON PROCESS CONTROL, PC

ISBN

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ISSN

2995-1720

e-ISSN

2995-1739

Number of pages

8

Pages from-to

—

Publisher name

IEEE

Place of publication

NEW YORK

Event location

Strbske Pleso

Event date

Jun 3, 2025

Type of event by nationality

EUR - Evropská akce

UT code for WoS article

001541574400007