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.

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call