Journal
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
Volume 48, Issue 12, Pages 2305-2314Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2017.2717866
Keywords
Associative memories; Lyapunov's direct method; quaternion matrix decomposition; quaternion-valued neural networks (QVNNs); semi-discretization technique
Funding
- Program of Chongqing Innovation Team Project in University [CXTDX201601022]
- Natural Science Foundation of Chongqing Municipal Education Commission [KJ1705138, KJ1600504]
- Natural Science Foundation of Chongqing [cstc2017jcyjA1353]
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This paper addresses the problem of designing associative memories based on quaternion-valued neural networks (QVNNs). A system designing procedure for QVNNs is developed by employing quaternion matrix decomposition, and a given set of states can be assigned as the equilibrium points of the designed QVNNs. Moreover, some sufficient conditions for the asymptotic stability of the equilibrium points are obtained via Lyapunov's direct method. Numerical simulations manifest that the constructed QVNNs work efficiently on storing and retrieving blurred gray-scale and true color images.
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