Research of Orange Quality Classification Technology Based on BP Neural Network Algorithm

Chun Hua Qian; He Qun Qiang; Sheng Rong Gong

doi:10.4028/www.scientific.net/AMM.713-715.1821

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Orange Feature Extraction and Description Based on Image Processing
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Recursive Particle Swarm Optimization Application for the Radial Basis Function Networks Modeling System
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Research of Orange Quality Classification Technology Based on BP Neural Network Algorithm
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Research of Topology Optimization of Nodal Density Based on Bilinear Interpolation
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Research on Automatic Text Classification Algorithm Based on ITF-IDF and KNN
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 713-715Research of Orange Quality Classification...

Research of Orange Quality Classification Technology Based on BP Neural Network Algorithm

Abstract:

BP algorithm is a classical neural network algorithm. We analyzed the deficiency of traditional BP neural network algorithm, designed new S function and momentum method strategy, optimized the algorithm parameters. We use the new algorithm in the classification of orange images, take color and shape features as input value, the experimental results proved that our algorithm is faster and the classification accuracy rate reaches to 90%

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Periodical:

Applied Mechanics and Materials (Volumes 713-715)

Pages:

1821-1824

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.713-715.1821

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Online since:

January 2015

Authors:

Chun Hua Qian*, He Qun Qiang, Sheng Rong Gong

Keywords:

BP Algorithm, Image Classification, Neural Network

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* - Corresponding Author

References

[1] David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Willams. Learning representations by back-propagating errors[J]. Nature 1986, Vol. 323, 533 – 536.

DOI: 10.1038/323533a0

Google Scholar

[2] Jing Guolin, Du Wenting, Guo Yingying. Studies on prediction of separation percent in electrodialysis process via BP neural networks and improved BP algorithms[J]. Desalination, 2012, Vol. 291, pp.78-93.

DOI: 10.1016/j.desal.2012.02.002

Google Scholar

[3] Xing Xiao-Shuai, Zhang Qing-Quan, Yang Pei-Lin, Li Chao, Li Chao, Chen Zhao-Yang. Research on BP algorithm based on conjugate gradient[J]. Information Science and Engineering, 2010, pp.5620-5623.

DOI: 10.1109/icise.2010.5691875

Google Scholar

[4] Dong Jin, Dong Jin, Wei Fajie, Wei Fajie. The application of the improved BP algorithm in the project investment prediction[J]. Information Science and Engineering, 2010, pp.6242-6245.

DOI: 10.1109/icise.2010.5690270

Google Scholar

[5] Jie Hu, Jie Hu, Xiangjin Zeng, Xiangjin Zeng. A hybrid PSO-BP algorithm and its application[J]. Natural Computation, 2010, Vol. 5, pp.2520-2523.

DOI: 10.1109/icnc.2010.5583289

Google Scholar

[6] A-Lei Chen, A-Lei Chen, Dang-Wei Wang, Dang-Wei Wang, Xiao-Yan Ma, Xiao-Yan Ma. An improved BP algorithm for high-resolution MIMO imaging radar[J]. Audio Language and Image Processing, 2010, pp.1663-1667.

DOI: 10.1109/cie-radar.2011.6159702

Google Scholar