Mittag-Leffler stability and application of delayed fractional-order competitive neural networks. Academic Article

abstract

• In the article, the Mittag-Leffler stability and application of delayed fractional-order competitive neural networks (FOCNNs) are developed. By virtue of the operator pair, the conditions of the coexistence of equilibrium points (EPs) are discussed and analyzed for delayed FOCNNs, in which the derived conditions of coexistence improve the existing results. In particular, these conditions are simplified in FOCNNs with stepped activations. Furthermore, the Mittag-Leffler stability of delayed FOCNNs is established by using the principle of comparison, which enriches the methodologies of fractional-order neural networks. The results on the obtained stability can be used to design the horizontal line detection of images, which improves the practicability of image detection results. Two simulations are displayed to validate the superiority of the obtained results.

authors

• Huang, Tingwen

published proceedings

• Neural Netw

author list (cited authors)

• Zhang, F., Huang, T., Wu, A., & Zeng, Z.

complete list of authors

• Zhang, Fanghai||Huang, Tingwen||Wu, Ailong||Zeng, Zhigang

publication date

• July 2024

publisher

• Elsevier  Publisher

published in

• Neural Networks  Journal

keywords

• Fractional-order Competitive Neural Networks
• Horizontal Line Detection
• Mittag-leffler Stability

PubMed Central ID

• 38986190

Digital Object Identifier (DOI)

• 10.1016/j.neunet.2024.106501

start page

• 106501

end page

• 106501

volume

• 179

URL

• http://dx.doi.org/10.1016/j.neunet.2024.106501