RESEARCH ON DEEP NEURAL NETWORK LEARNING BASED ON IMPROVED BP ALGORITHM
Deep learning can make the computing model that contains a number of processing layers to learn the data that contains many levels of abstract representation.This kind of learning way in the most advanced speech recognition,visual object recognition,object detection and many other areas,such as biol...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2018-01-01
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| Series: | Jixie qiangdu |
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| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2018.04.006 |
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| author | HUANG Pei |
| author_facet | HUANG Pei |
| author_sort | HUANG Pei |
| collection | DOAJ |
| description | Deep learning can make the computing model that contains a number of processing layers to learn the data that contains many levels of abstract representation.This kind of learning way in the most advanced speech recognition,visual object recognition,object detection and many other areas,such as biology,genetics and medicine brought significant improvement.Deep learning can find the complex structure of large data,and the convolution neural network as one of the important models of the depth study in the processing of voice,image,video and text,and other aspects of a new breakthrough.It is the use of BP algorithm to guide the machine how to get the error before the layer to adjust the parameters of this layer,so that these parameters are more conducive to the calculation of the model.In view of the shortcomings of traditional BP algorithm,a fast BP algorithm is proposed,which has the disadvantages of slow convergence speed and often falls into local minimum points.The improved convolutional neural network is used to validate the data set MNIST,English character recognition and medical image.The simulation results show the effectiveness of the proposed algorithm. |
| format | Article |
| id | doaj-art-108d2fc41f55454cad76c6c40b8a4e47 |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2018-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-108d2fc41f55454cad76c6c40b8a4e472025-01-15T02:31:49ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692018-01-014079680130602453RESEARCH ON DEEP NEURAL NETWORK LEARNING BASED ON IMPROVED BP ALGORITHMHUANG PeiDeep learning can make the computing model that contains a number of processing layers to learn the data that contains many levels of abstract representation.This kind of learning way in the most advanced speech recognition,visual object recognition,object detection and many other areas,such as biology,genetics and medicine brought significant improvement.Deep learning can find the complex structure of large data,and the convolution neural network as one of the important models of the depth study in the processing of voice,image,video and text,and other aspects of a new breakthrough.It is the use of BP algorithm to guide the machine how to get the error before the layer to adjust the parameters of this layer,so that these parameters are more conducive to the calculation of the model.In view of the shortcomings of traditional BP algorithm,a fast BP algorithm is proposed,which has the disadvantages of slow convergence speed and often falls into local minimum points.The improved convolutional neural network is used to validate the data set MNIST,English character recognition and medical image.The simulation results show the effectiveness of the proposed algorithm.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2018.04.006Deep learningConvolutional neural networkImproved BP algorithm |
| spellingShingle | HUANG Pei RESEARCH ON DEEP NEURAL NETWORK LEARNING BASED ON IMPROVED BP ALGORITHM Jixie qiangdu Deep learning Convolutional neural network Improved BP algorithm |
| title | RESEARCH ON DEEP NEURAL NETWORK LEARNING BASED ON IMPROVED BP ALGORITHM |
| title_full | RESEARCH ON DEEP NEURAL NETWORK LEARNING BASED ON IMPROVED BP ALGORITHM |
| title_fullStr | RESEARCH ON DEEP NEURAL NETWORK LEARNING BASED ON IMPROVED BP ALGORITHM |
| title_full_unstemmed | RESEARCH ON DEEP NEURAL NETWORK LEARNING BASED ON IMPROVED BP ALGORITHM |
| title_short | RESEARCH ON DEEP NEURAL NETWORK LEARNING BASED ON IMPROVED BP ALGORITHM |
| title_sort | research on deep neural network learning based on improved bp algorithm |
| topic | Deep learning Convolutional neural network Improved BP algorithm |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2018.04.006 |
| work_keys_str_mv | AT huangpei researchondeepneuralnetworklearningbasedonimprovedbpalgorithm |