标题： 具有相位和幅度量化的新颖复值霍普菲尔德神经网络 显示英文标题

标题： Novel Complex-Valued Hopfield Neural Networks with Phase and Magnitude Quantization

摘要： 这篇研究论文介绍了两种新颖的复值霍普菲尔德神经网络（CvHNNs），它们结合了相位和幅度量化。 第一个CvHNN采用一种基于复数网络贡献的矩形坐标表示的上限型激活函数。 第二个CvHNN同样结合了相位和幅度量化，但使用一种基于复数网络贡献的极坐标表示的上限型激活函数。 所提出的CvHNNs通过其相位和幅度量化，显著增加了状态数量，从而扩展了CvHNNs的潜在应用范围。 显示英文摘要

摘要： 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.