Modulation Recognition Method of Complex Modulation Signal Based on Convolution Neural Network

2020 ◽  
Author(s):  
Rui Liu ◽  
Yunxin Guo ◽  
Shibing Zhu
Keyword(s):  
Neural Network ◽  
Convolution Neural Network ◽  
Modulation Recognition ◽  
Recognition Method ◽  
Modulation Signal ◽  
Complex Modulation

scholarly journals Modulation Recognition of Communication Signal Based on Convolutional Neural Network

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2302
Author(s):  
Kaiyuan Jiang ◽  
Xvan Qin ◽  
Jiawei Zhang ◽  
Aili Wang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Communication Systems ◽  
Learning Algorithm ◽  
Recognition Task ◽  
Machine Learning Algorithms ◽  
Convolution Neural Network ◽  
Modulation Recognition ◽  
Signal Modulation ◽  
Communication Signal

In the noncooperation communication scenario, digital signal modulation recognition will help people to identify the communication targets and have better management over them. To solve problems such as high complexity, low accuracy and cumbersome manual extraction of features by traditional machine learning algorithms, a kind of communication signal modulation recognition model based on convolution neural network (CNN) is proposed. In this paper, a convolution neural network combines bidirectional long short-term memory (BiLSTM) with a symmetrical structure to successively extract the frequency domain features and timing features of signals and then assigns importance weights based on the attention mechanism to complete the recognition task. Seven typical digital modulation schemes including 2ASK, 4ASK, 4FSK, BPSK, QPSK, 8PSK and 64QAM are used in the simulation test, and the results show that, compared with the classical machine learning algorithm, the proposed algorithm has higher recognition accuracy at low SNR, which confirmed that the proposed modulation recognition method is effective in noncooperation communication systems.

An E-nose and Convolution Neural Network based Recognition Method for Processed Products of Crataegi Fructus

2020 ◽  
Vol 23 ◽  
Author(s):  
Tianshu Wang ◽  
Yanpin Chao ◽  
Fangzhou Yin ◽  
Xichen Yang ◽  
Chenjun Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Electronic Nose ◽  
Experimental Results ◽  
Convolution Neural Network ◽  
New Method ◽  
Recognition Method ◽  
Processed Products

Background: The identification of Fructus Crataegi processed products manually is inefficient and unreliable. Therefore, how to identify the Fructus Crataegis processed products efficiently is important. Objective: In order to efficiently identify Fructus Grataegis processed products with different odor characteristics, a new method based on an electronic nose and convolutional neural network is proposed. Methods: First, the original smell of Fructus Grataegis processed products is obtained by using the electronic nose and then preprocessed. Next, feature extraction is carried out on the preprocessed data through convolution pooling layer Results: The experimental results show that the proposed method has higher accuracy for the identification of Fructus Grataegis processed products, and is competitive with other machine learning based methods. Conclusion: The method proposed in this paper is effective for the identification of Fructus Grataegi processed products.

scholarly journals Application of Convolution Neural Network (CNN) Model Combined with Pyramid Algorithm in Aerobics Action Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qi Liang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Action Recognition ◽  
Network Model ◽  
Neural Network Model ◽  
Convolution Neural Network ◽  
Recognition Method ◽  
Recognition Model ◽  
Recognition Function ◽  
The Common

In order to realize high-accuracy recognition of aerobics actions, a highly applicable deep learning model and faster data processing methods are required. Therefore, it is a major difficulty in the field of research on aerobics action recognition. Based on this, this paper studies the application of the convolution neural network (CNN) model combined with the pyramid algorithm in aerobics action recognition. Firstly, the basic architecture of the convolution neural network model based on the pyramid algorithm is proposed. Combined with the application strategy of the common recognition model in aerobics action recognition, the traditional aerobics action capture information is processed. Through the characteristics of different aerobics actions, different accurate recognition is realized, and then, the error of the recognition model is evaluated. Secondly, the composite recognition function of the convolution neural network model in this application is constructed, and the common data layer effect recognition method is used in the optimization recognition. Aiming at the shortcomings of the composite recognition function, the pyramid algorithm is used to improve the convolution neural network recognition model by deep learning optimization. Finally, through the effectiveness comparison experiment, the results show that the convolution neural network model based on the pyramid algorithm is more efficient than the conventional recognition method in aerobics action recognition.

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