scholarly journals Improved Algorithm Based on The Deep Integration of Googlenet and Residual Neural Network

2021 ◽  
Vol 1757 (1) ◽  
pp. 012069
Author(s):  
Xuehua Huang ◽  
Weihong Chen ◽  
Wangdong Yang
Keyword(s):  
Neural Network ◽  
Deep Integration ◽  
Improved Algorithm

scholarly journals An improved algorithm for neural network classification of imbalanced training sets

1993 ◽  
Vol 4 (6) ◽  
pp. 962-969 ◽  
Author(s):  
R. Anand ◽  
K.G. Mehrotra ◽  
C.K. Mohan ◽  
S. Ranka
Keyword(s):  
Neural Network ◽  
Neural Network Classification ◽  
Training Sets ◽  
Improved Algorithm

Research on Intelligent Forecasting of Financial Market Parameter Time Series Based on Deep Learning

2021 ◽  
Vol 7 (5) ◽  
pp. 4596-4607
Author(s):  
Enyang Zhu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
Financial Markets ◽  
Intelligent System ◽  
System Modeling ◽  
Layer By Layer ◽  
Healthy Development ◽  
The Neural Network ◽  
Deep Integration

Objectives: Deep learning has become the most representative and potential intelligent system modeling technology in artificial intelligence. However, the complexity of financial markets goes far beyond all economic games. Methods: This paper is devoted to the feasibility and efficiency of the deep-integration neural network model as one of the main paradigms of in-depth learning in the intelligent prediction of financial time. A prediction model of stack self-coding neural network composed of bottom stack self-coding and top regression neurons is proposed. Results: Firstly, the self-encoder unsupervised learning mechanism is used to identify and learn the time series, and the layers of the neural network are learned greedy layer by layer. Then the stack self-encoder is extended to the SAEP model with supervised mechanism, and the parameters learned by SAE are used. Used to initialize the neural network, and finally use the supervised learning to fine-tune the weights. Conclusion: The research results show that the model provides effective financial planning and decision-making basis for financial forecasting, maintains the healthy development of financial markets, and maximizes the benefits of profit-making institutions.

Application of Improved BPNN to Damage Detection of Composite Materials

2011 ◽  
Vol 467-469 ◽  
pp. 1097-1101
Author(s):  
Xiao Ma Dong
Keyword(s):  
Neural Network ◽  
Composite Materials ◽  
Damage Detection ◽  
Bp Neural Network ◽  
Dynamic Method ◽  
Damage Identification ◽  
Wavelet Series ◽  
Improved Algorithm

A dynamic method based on improved algorithm BP neural network for damage identification of composite materials was proposed. By using wavelet series, the features of signals were extracted and input to improved algorithm BP neural network for training the network and identifying the damages. Finally, the experiment results show that this proposed method can exactly identify the faults of composite materials.

Natural Gas Load Forecasting Based on Improved Back Propagation Neural Network

2014 ◽  
Vol 563 ◽  
pp. 312-315
Author(s):  
Yu Lian Jiang
Keyword(s):  
Neural Network ◽  
Natural Gas ◽  
Relative Error ◽  
Load Forecasting ◽  
Back Propagation ◽  
Absolute Error ◽  
Back Propagation Neural Network ◽  
Back Propagation Algorithm ◽  
Traditional Algorithm ◽  
Improved Algorithm

To suit for the condition that the relative error is more popular than the absolute error, and overcome the shortcoming of the traditional Back propagation neural network, this paper proposed an improved Back propagation algorithm with additional momentum item based on the sum of relative error square. The improved algorithm was applied to the example of the natural gas load forecasting, simulations showed that the improved algorithm has faster training speed than the traditional algorithm, and has higher accuracy as while.

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