TY - GEN

T1 - Network topology identification using supervised pattern recognition neural networks

AU - Perumalla, Aniruddha

AU - Koru, Ahmet Taha

AU - Johnson, Eric Norman

N1 - Publisher Copyright:
© 2021 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved

PY - 2021

Y1 - 2021

N2 - This paper studies the network topology identification of multi-agent systems with single-integrator dynamics using supervised pattern recognition networks. We split the problem into two classes: (i) small-scale systems, and (ii) large-scale systems. In the small-scale case, we generate all connected (undirected) graphs. A finite family of vectors represent all possible initial conditions by gridding the interval 0 and 1 for each agent. The system responses for all graphs with all initial conditions are the training data for the supervised pattern recognition neural network. This network is successful in identification of the most connected node in up to nearly 99% of cases involving small-scale systems. We present the accuracy of the trained network for network topology identification with respect to grid space. Then, an algorithm predicated on the pattern recognition network, which is trained for a small-scale system, identifies the most connected node in large-scale systems. Monte Carlo simulations estimate the accuracy of the algorithm. We also present the results for these simulations, which demonstrate that the algorithm succeeds in finding the most connected node in more than 60% of the test cases.

AB - This paper studies the network topology identification of multi-agent systems with single-integrator dynamics using supervised pattern recognition networks. We split the problem into two classes: (i) small-scale systems, and (ii) large-scale systems. In the small-scale case, we generate all connected (undirected) graphs. A finite family of vectors represent all possible initial conditions by gridding the interval 0 and 1 for each agent. The system responses for all graphs with all initial conditions are the training data for the supervised pattern recognition neural network. This network is successful in identification of the most connected node in up to nearly 99% of cases involving small-scale systems. We present the accuracy of the trained network for network topology identification with respect to grid space. Then, an algorithm predicated on the pattern recognition network, which is trained for a small-scale system, identifies the most connected node in large-scale systems. Monte Carlo simulations estimate the accuracy of the algorithm. We also present the results for these simulations, which demonstrate that the algorithm succeeds in finding the most connected node in more than 60% of the test cases.

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M3 - Conference contribution

AN - SCOPUS:85103815673

T3 - ICAART 2021 - Proceedings of the 13th International Conference on Agents and Artificial Intelligence

SP - 258

EP - 264

BT - ICAART 2021 - Proceedings of the 13th International Conference on Agents and Artificial Intelligence

A2 - Rocha, Ana Paula

A2 - Steels, Luc

A2 - van den Herik, Jaap

PB - SciTePress

T2 - 13th International Conference on Agents and Artificial Intelligence, ICAART 2021

Y2 - 4 February 2021 through 6 February 2021

ER -