前馈多层相位神经网络

I. Pavaloiu, A. Vasile, Sebastian Marius Rosu, G. Dragoi
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引用次数: 2

摘要

复值神经网络(CVNNs)是使用复数起作用的人工神经网络(ann),它们具有复值参数并接受复值输入。基于相位的神经元(PBNs)是一种简单的cvnn,它使用模数为1的复数作为内部权重,唯一可适应的参数是权重的相位。本文提出了连续相基神经网络(CPBN)的一些局限性,并描述了前馈多层相基神经网络(MLPBN)的结构及其使用自适应反向传播算法的训练。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Feedforward multilayer phase-based neural networks
Complex-Valued Neural Networks (CVNNs) are Artificial Neural Networks (ANNs) which function using complex numbers - they have complex-valued parameters and accept complex-valued inputs. Phase-Based Neurons (PBNs) are simple CVNNs that use for the internal weights complex numbers with the modulus 1, the only adaptable parameters being the phases of the weights. We present in this paper some limitations of the Continuous Phase-Based Neuron (CPBN) and describe the structure of a Feedforward Multilayer Phase-Based Neural Network (MLPBN) and its training using an adaptation of the backpropagation algorithm.
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