NDLI: Training and application of process neural network based on quantum-behaved evolutionary algorithm

Content Provider	IEEE Xplore Digital Library
Author	Zhang Qiang Li Pan-chi
Copyright Year	2012
Description	Author affiliation: Sch. of Comput. & Inf. Technol., Northeast Pet. Univ., Daqing, China (Zhang Qiang; Li Pan-chi)
Abstract	Aiming at the problem that it is difficult for BP algorithm to converge because of more parameters in training of process neural networks based on orthogonal basis expansion, a quantum-behaved evolutionary algorithm is presented which combines the quantum theory and is to train the process neural network. The algorithm using the Pauli matrices to establish the axis of rotation, using qubits in Bloch sphere to rotate around the axis method to carry out optimal search, using the Hadamard gate achieve individual variability to avoid premature, and effectively overcomes the complex compute and easily plunges into local minimums about BP algorithm. Taking network traffic prediction as an application, the simulation results show that the algorithm is validity.
Sponsorship	IEEE Harbin. Sect.
Starting Page	929
Ending Page	934
File Size	419362
Page Count	6
File Format	PDF
ISBN	9781467329637
e-ISBN	9781467329644
DOI	10.1109/ICCSNT.2012.6526079
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Quantum Prediction Process Neural Network Network traffic flow Algorithm
Content Type	Text
Resource Type	Article

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