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Dynamics of BAM neural networks with mixed delays and leakage time‐varying delays in the weighted pseudo–almost periodic on time‐space scales
Author(s) -
Arbi Adnène
Publication year - 2018
Publication title -
mathematical methods in the applied sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.719
H-Index - 65
eISSN - 1099-1476
pISSN - 0170-4214
DOI - 10.1002/mma.4661
Subject(s) - mathematics , bidirectional associative memory , exponential stability , convergence (economics) , artificial neural network , complement (music) , leakage (economics) , stability (learning theory) , control theory (sociology) , content addressable memory , computer science , nonlinear system , biochemistry , chemistry , physics , phenotype , macroeconomics , control (management) , quantum mechanics , machine learning , complementation , artificial intelligence , economics , gene , economic growth
Bidirectional associative memory models are 2‐layer heteroassociative networks. In this work, we prove the existence and the global exponential stability of the unique weighted pseudo–almost periodic solution of bidirectional associative memory neural networks with mixed time‐varying delays and leakage time‐varying delays on time‐space scales. Some sufficient conditions are given for the existence, the convergence, and the global exponential stability of the weighted pseudo–almost periodic solution by using fixed‐point theorem and differential inequality techniques. The results of this paper complement the previous outcomes. An example is given to show the effectiveness of the derived results via computer simulations.