Version 1
: Received: 6 September 2024 / Approved: 6 September 2024 / Online: 9 September 2024 (09:12:56 CEST)
How to cite:
Ullah, A.; Shuaib, M. Neural Network Models Synchronization and Stability through Calculated Incommensurate Fractional-Orders. Preprints2024, 2024090654. https://doi.org/10.20944/preprints202409.0654.v1
Ullah, A.; Shuaib, M. Neural Network Models Synchronization and Stability through Calculated Incommensurate Fractional-Orders. Preprints 2024, 2024090654. https://doi.org/10.20944/preprints202409.0654.v1
Ullah, A.; Shuaib, M. Neural Network Models Synchronization and Stability through Calculated Incommensurate Fractional-Orders. Preprints2024, 2024090654. https://doi.org/10.20944/preprints202409.0654.v1
APA Style
Ullah, A., & Shuaib, M. (2024). Neural Network Models Synchronization and Stability through Calculated Incommensurate Fractional-Orders. Preprints. https://doi.org/10.20944/preprints202409.0654.v1
Chicago/Turabian Style
Ullah, A. and Muhammad Shuaib. 2024 "Neural Network Models Synchronization and Stability through Calculated Incommensurate Fractional-Orders" Preprints. https://doi.org/10.20944/preprints202409.0654.v1
Abstract
Artificial neural networks are highly efficient tools for numerous kinds of tasks, which include forecasting, regression, and recognizing patterns in classification and pattern recognition. An artificial neural network is a model for processing information using essential components called artificial neurons that are inspired by the human brain. Two significant aspects of delayed neural networks dynamic activity are their stability and the synchronization phenomenon. This work presents a bidirectional associative memory (BAM) incommensurate fractional-order delay neural network model. The incommensurate fractional orders are calculated within the stability region through the corresponding eigenvalues and their points of equilibrium. The involved neuron states are synchronized with each other for the calculated incommensurate stable fractional orders. The reduction of the time delay will give more relaxation time for the state variable $w_4(t)$ to be stable as compared to the state variable $w_3(t)$ due to the induction of extra activation functions. The theoretical findings of this research have significance for providing guidance and controlling the dynamic behavior of artificial neural networks.
Computer Science and Mathematics, Computational Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
