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Annual Conference of the IEEE Industrial Electronics Society (IECON)

Controller application of a multi-layer quantum neural network trained by a conjugate gradient algorithm

Kazuhiko Takahashi, Motoki Kurokawa, Masafumi Hashimoto

Published Nov 1, 2011• 5 citations
Publisher

Abstract

This paper investigates a quantum neural network and discusses its application to control systems. A learning-type neural control system that uses a multi-layer quantum neural network having qubit neurons as its information processing unit is proposed. A conjugate gradient algorithm is applied instead of the back-propagation algorithm for the supervised training of the multi-layer quantum neural network in order to improve learning performance. To evaluate the capability of the learning-type quantum neural control system, computational experiments are conducted for controlling a nonholonomic system - in this study a two-wheeled robot. Simulation results confirm both feasibility and robustness of the learning-type quantum neural control system.

DOI
10.1109/iecon.2011.6119677

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