Remarks on model reference self-tuning PID controller using quantum neural network with qubit neurons

Takahashi, Kazuhiko; Shiotani, Yuka; Hashimoto, Masafiimi

doi:10.1109/socpar.2013.7054138

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…The fusion of artificial neural network and quantum theory can better simulate the process of human brain information processing. Quantum neural network can improve the approximation ability and information processing efficiency of artificial neural network, which had been used for many applications [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting Model Based on Quantum Elman Neural Networks

Zhang

Gong²

2016

Mathematical Problems in Engineering

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Short-term load forecasting model based on quantum Elman neural networks was constructed in this paper. The quantum computation and Elman feedback mechanism were integrated into quantum Elman neural networks. Quantum computation can effectively improve the approximation capability and the information processing ability of the neural networks. Quantum Elman neural networks have not only the feedforward connection but also the feedback connection. The feedback connection between the hidden nodes and the context nodes belongs to the state feedback in the internal system, which has formed specific dynamic memory performance. Phase space reconstruction theory is the theoretical basis of constructing the forecasting model. The training samples are formed by means ofK-nearest neighbor approach. Through the example simulation, the testing results show that the model based on quantum Elman neural networks is better than the model based on the quantum feedforward neural network, the model based on the conventional Elman neural network, and the model based on the conventional feedforward neural network. So the proposed model can effectively improve the prediction accuracy. The research in the paper makes a theoretical foundation for the practical engineering application of the short-term load forecasting model based on quantum Elman neural networks.

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Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting Model Based on Quantum Elman Neural Networks

Zhang

Gong²

2016

Mathematical Problems in Engineering

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Self-tuning PID controller using quantum neural network with qubit-inspired neurons

Takahashi

Shiotani

Hashimoto

2015

HIS

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This paper investigates the performance of an adaptive controller using a multi-layer quantum neural network (QNN) comprising qubit-inspired neurons as information processing units. The control system is a self-tuning controller whose control parameters are tuned online by the QNN to track plant output relative to the desired plant output generated by a reference model. A proportional-integral-derivative (PID) controller is utilized as a conventional controller, with its parameters tuned by the QNN. Computational experiments to control a single-input single-output discrete-time non-linear plant are conducted to evaluate the capability and characteristics of the quantum neural self-tuning controller. The experiment results demonstrate the feasibility and effectiveness of the proposed controller.

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Remarks on model reference self-tuning PID controller using quantum neural network with qubit neurons

Cited by 2 publications

References 11 publications

Short-Term Load Forecasting Model Based on Quantum Elman Neural Networks

Short-Term Load Forecasting Model Based on Quantum Elman Neural Networks

Self-tuning PID controller using quantum neural network with qubit-inspired neurons

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