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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 362150, 11 pages
Research Article

Model and Algorithm of BP Neural Network Based on Expanded Multichain Quantum Optimization

1PLA University of Science and Technology, Nanjing 210007, China
2Bengbu Automobile NCO Academy, Bengbu 233011, China
3Logistics Academy, Beijing 100858, China

Received 30 August 2015; Accepted 15 October 2015

Academic Editor: Peter Dabnichki

Copyright © 2015 Baoyu Xu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The model and algorithm of BP neural network optimized by expanded multichain quantum optimization algorithm with super parallel and ultra-high speed are proposed based on the analysis of the research status quo and defects of BP neural network to overcome the defects of overfitting, the random initial weights, and the oscillation of the fitting and generalization ability along with subtle changes of the network parameters. The method optimizes the structure of the neural network effectively and can overcome a series of problems existing in the BP neural network optimized by basic genetic algorithm such as slow convergence speed, premature convergence, and bad computational stability. The performance of the BP neural network controller is further improved. The simulation experimental results show that the model is with good stability, high precision of the extracted parameters, and good real-time performance and adaptability in the actual parameter extraction.