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1 January 2015 Small Time-Scale Network Tidal Water Level Forecasting Based on Quantum Neural Network
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Abstract

Zhao, K.; Wang, H., and Guo, T., 2015. Small time-scale network traffic prediction on the basis of Quantum Neural Network.

This paper proposed a quantum neural network model consist of quantum bit, universal quantum gates and quantum weighted, gave the learning algorithm based on improved PRP conjugate gradient, in order to improve the convergence speed of the network and the network performance, and further proved the global convergence of the algorithm in theory. Compared with the existing localized support vector machine (SVM) regression and flexible neural tree model, the QNN model proposed in this paper have a higher prediction precision and low computational complexity for small scale tidal water level prediction, its convergence speed and robustness are better than BP network and quantum weighted neural network.

© 2015 Coastal Education and Research Foundation
Kai Zhao, Hui Wang, and Tong Guo "Small Time-Scale Network Tidal Water Level Forecasting Based on Quantum Neural Network," Journal of Coastal Research 73(sp1), 815-822, (1 January 2015). https://doi.org/10.2112/SI73-140.1
Received: 15 August 2014; Accepted: 13 November 2014; Published: 1 January 2015
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