{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T17:55:52Z","timestamp":1772819752563,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T00:00:00Z","timestamp":1737676800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Easwari Engineering College","award":["SRM\/EEC\/RI\/006"],"award-info":[{"award-number":["SRM\/EEC\/RI\/006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The present research investigates the global asymptotic stability of bidirectional associative memory (BAM) neural networks using distinct sufficient conditions. The primary objective of this study is to establish new generalized criteria for the global asymptotic robust stability of time-delayed BAM neural networks at the equilibrium point, utilizing the Frobenius norm and the positive symmetrical approach. The new sufficient conditions are derived with the help of the Lyapunov\u2013Krasovskii functional and the Frobenius norm, which are important in deep learning for a variety of reasons. The derived conditions are not influenced by the system parameter delays of the BAM neural network. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed conclusions regarding network parameters.<\/jats:p>","DOI":"10.3390\/sym17020183","type":"journal-article","created":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T04:59:10Z","timestamp":1737953950000},"page":"183","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Frobenius Norm-Based Global Stability Analysis of Delayed Bidirectional Associative Memory Neural Networks"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1719-7148","authenticated-orcid":false,"given":"N. Mohamed","family":"Thoiyab","sequence":"first","affiliation":[{"name":"Department of Mathematics, Jamal Mohamed College, Affiliated to Bharathidasan University, Tiruchirappalli 620020, Tamil Nadu, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5016-7560","authenticated-orcid":false,"given":"Saravanan","family":"Shanmugam","sequence":"additional","affiliation":[{"name":"Center for Computational Biology, Easwari Engineering College, Chennai 600089, Tamil Nadu, India"},{"name":"Center for Research, SRM Institute of Science and Technology-Ramapuram, Chennai 600089, Tamil Nadu, India"}]},{"given":"Rajarathinam","family":"Vadivel","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Faculty of Science and Technology, Phuket Rajabhat University, Phuket 83000, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0375-7917","authenticated-orcid":false,"given":"Nallappan","family":"Gunasekaran","sequence":"additional","affiliation":[{"name":"Eastern Michigan Joint College of Engineering, Beibu Gulf University, Qinzhou 535011, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5718","DOI":"10.1038\/s41467-024-48069-8","article-title":"Network properties determine neural network performance","volume":"15","author":"Jiang","year":"2024","journal-title":"Nat. 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