This paper investigates quantum neural networks and discusses its application to controlling systems. Multi-layer quantum neural networks having qubit neurons as its information processing unit are considered and a direct neural network controller using the multi-layer quantum neural networks is proposed. A real-coded genetic algorithm is applied instead of a back-propagation algorithm for the supervised training of the multi-layer quantum neural networks to improve learning performance. To evaluate the capability of the direct quantum neural network controller, computational experiments are conducted for controlling a discrete-time system and a nonholonomic system - in this study a two-wheeled robot. Experimental results confirm the effectiveness of the real-coded genetic algorithm for the training of the quantum neural networks and show both feasibility and robustness of the direct quantum neural control system. © 2012 Springer-Verlag.
CITATION STYLE
Takahashi, K., Kurokawa, M., & Hashimoto, M. (2012). Remarks on multi-layer quantum neural network controller trained by real-coded genetic algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7202 LNCS, pp. 50–57). https://doi.org/10.1007/978-3-642-31919-8_7
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