Remarks on an adaptive-type parallel controller using quantum neural network with qubit neurons

Kazuhiko Takahashi
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Abstract

This paper presents an adaptive-type parallel controller based on a quantum neural network and investigates its characteristics for control systems. A multi-layer quantum neural network that uses qubit neurons as an information processing unit is utilized to design the adaptive-type parallel controller that conducts the training of the quantum neural network as an online process. Computational experiments to control the single-input single-output nonlinear discrete time plant are conducted in order to evaluate the learning performance and capability of the adaptive-type quantum neural parallel controller. The results of the computational experiments confirm both the feasibility and the effectiveness of the adaptive-type quantum neural parallel controller.
基于量子比特神经元的量子神经网络自适应并行控制器的研究
提出了一种基于量子神经网络的自适应并行控制器,并研究了其控制系统的特性。利用以量子比特神经元为信息处理单元的多层量子神经网络,设计了自适应型并行控制器,将量子神经网络的训练作为在线过程进行。为了评价自适应型量子神经并行控制器的学习性能和控制能力,对单输入单输出非线性离散时间对象进行了控制计算实验。计算实验结果验证了自适应型量子神经并行控制器的可行性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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