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- W2892025901 abstract "The complex-valued Hopfield neural network (CHNN) can deal with multi-level information, and has often been applied to the storage of image data. The quaternion Hopfield neural network (QHNN) is a multistate model of a Hopfield neural network, and requires half the connection weight parameters of CHNN. In this study, we propose a commutative quaternion Hopfield neural network (CQHNN) as the analogy of QHNN. The multiplication of commutative quaternions is commutative and convenient, unlike that of quaternions. We compared the noise tolerance of CQHNNs and QHNNs by computer simulation, and discuss the simulation results from the perspective of rotational invariance and self loops." @default.
- W2892025901 created "2018-09-27" @default.
- W2892025901 creator A5025747344 @default.
- W2892025901 date "2018-12-01" @default.
- W2892025901 modified "2023-10-13" @default.
- W2892025901 title "Twin-multistate commutative quaternion Hopfield neural networks" @default.
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- W2892025901 doi "https://doi.org/10.1016/j.neucom.2018.09.023" @default.
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