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- W2624112727 abstract "In the present work, a Hopfield neural network with a new quaternionic activation function, referred to as a twin-multistate activation function, is proposed. The multistate activation function has been used in complex-valued Hopfield neural networks (CHNNs). It is useful for representing multilevel information, and the CHNNs with a multistate activation function have been applied to the storage of multilevel data, such as gray-scale images. A twin-multistate activation function consists of two multistate activation functions. Quaternionic Hopfield neural networks (QHNNs) with a twin-multistate activation function can take the place of the CHNNs with a multistate activation function. The QHNNs require half the number of connection parameters of CHNNs. Projection rule is a fast learning algorithm, and is suitable under restricted computational power. However, it requires full-connection, and the sparse connections are not allowed. Projection rule is also available for the QHNNs with a twin-multistate activation function. When the memory resource and computational power are restricted, the QHNNs with a twin-multistate activation function is useful. In the present work, the conventional network topology is only considered. To take advantage of non-commutativity of quaternions, several researchers proposed network topology for QHNNs, such as dual connections. In future, the QHNNs with a twin-multistate activation function will be extended to those with such network topology." @default.
- W2624112727 created "2017-06-15" @default.
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- W2624112727 date "2017-12-01" @default.
- W2624112727 modified "2023-10-17" @default.
- W2624112727 title "Quaternionic Hopfield neural networks with twin-multistate activation function" @default.
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- W2624112727 doi "https://doi.org/10.1016/j.neucom.2017.06.013" @default.
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