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- W2893236851 abstract "W373 the development of information technology more and more rapidly, the development of image recognition classification technology is also increasingly improved. This paper proposes a method which combines random Dropout algorithm with PSO-BP to realize the recognition and classification of small images. On the basis of the error back propagation adjustment weight of the traditional BP algorithm, the weight correction of PSO algorithm is introduced, and the PSO-BP network model is established. Then the hidden layer unit of PSO-BP network is improved by using stochastic Dropout algorithm, so the fast operation speed is obtained. The results show that compared with the traditional BP and the PSO-BP algorithm, the network error rate is obviously reduced after the algorithm is improved in this paper, and the real-time performance of the algorithm is improved to a certain extent." @default.
- W2893236851 created "2018-10-05" @default.
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- W2893236851 date "2018-05-01" @default.
- W2893236851 modified "2023-09-25" @default.
- W2893236851 title "Small Image Recognition Classification Based on Random Dropout and PSO-BP" @default.
- W2893236851 doi "https://doi.org/10.1109/imcec.2018.8469437" @default.
- W2893236851 hasPublicationYear "2018" @default.
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