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Novel Complex-Valued Hopfield Neural Networks with Phase and Magnitude Quantization
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本文提出两种新型的复值霍普菲尔德神经网络(CvHNNs),结合相位和幅度量化,显著增加状态数量,拓展CvHNNs应用范围。

arXiv:2507.00461v1 Announce Type: cross Abstract: This research paper introduces two novel complex-valued Hopfield neural networks (CvHNNs) that incorporate phase and magnitude quantization. The first CvHNN employs a ceiling-type activation function that operates on the rectangular coordinate representation of the complex net contribution. The second CvHNN similarly incorporates phase and magnitude quantization but utilizes a ceiling-type activation function based on the polar coordinate representation of the complex net contribution. The proposed CvHNNs, with their phase and magnitude quantization, significantly increase the number of states compared to existing models in the literature, thereby expanding the range of potential applications for CvHNNs.

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复值霍普菲尔德神经网络 相位量化 幅度量化 状态数量 应用拓展
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