Quaternion-Valued Twin-Multistate Hopfield Neural Networks With Dual Connections

被引：10

作者：

Kobayashi, Masaki ^{[1
]}

机构：

[1] Univ Yamanashi, Ctr Math Sci, Yamanashi 4008511, Japan

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2021年 / 32卷 / 02期

关键词：

Neurons; Quaternions; Biological neural networks; Training; Computer simulation; Hopfield neural networks; Artificial neural networks; Complex-valued Hopfield neural networks (CHNNs); dual connection (DC); multistate neuron; quaternion; rotational invariance; CAPACITY;

D O I：

10.1109/TNNLS.2020.2979904

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Dual connections (DCs) utilize the noncommutativity of quaternions and improve the noise tolerance of quaternion Hopfield neural networks (QHNNs). In this article, we introduce DCs to twin-multistate QHNNs. We conduct computer simulations to investigate the noise tolerance. The QHNNs with DCs were weak against an increase in the number of training patterns, but they were robust against increased resolution factor. The simulation results can be explained from the standpoints of storage capacities and rotational invariance.

引用

页码：892 / 899

页数：8

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