scholarly journals A BP Neural Network Method for Grade Classification of Loose Damage in Semirigid Pavement Bases

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Bei Zhang ◽  
Jianyang Liu ◽  
Yanhui Zhong ◽  
Xiaolong Li ◽  
Meimei Hao ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Electromagnetic Waves ◽  
Fdtd Method ◽  
Simulated Data ◽  
Radar Data ◽  
Base Layer ◽  
Identification Method ◽  
Network Identification ◽  
Base Course

This study aims to address the problem that loose damage of the pavement base course cannot currently be quantitatively identified, and thus the classification and recognition of the extent of looseness mainly rely on empirical judgments. Based on the finite-difference time-domain (FDTD) method, a backpropagation (BP) neural network identification method for loose damage of a semirigid base is presented. The FDTD method is used to simulate a semirigid base road model numerically with different degrees of looseness, and the eigenvalue parameters for recognition of the presence and extent of the looseness of the base layer are obtained. Then, a BP neural network identification method is used to classify and identify the loose damage of the base course. The results show that the classification and recognition of simulated electromagnetic waves have an accuracy of over 90%; the classification and recognition of radar data from an actual project have a recognition accuracy of over 80%. The good agreement between the classification and recognition results for the simulated data and measured data verifies the feasibility of the classification and recognition method, which can provide a new method for the use of ground-penetrating radar to detect loose damage and the extent of looseness in the base.

A deep residual neural network identification method for uneven dust accumulation on photovoltaic (PV) panels

Energy ◽  
2021 ◽  
pp. 122302
Author(s):  
Siyuan Fan ◽  
Yu Wang ◽  
Shengxian Cao ◽  
Bo Zhao ◽  
Tianyi Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Identification Method ◽  
Network Identification ◽  
Dust Accumulation

scholarly journals Radar Working State Recognition Based on the Unsupervised and Incremental Method

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Huiqin Li ◽  
Yanling Li ◽  
Chuan He ◽  
Hui Zhang ◽  
Jianwei Zhan
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Nearest Neighbor ◽  
Pso Algorithm ◽  
Radar Data ◽  
Damping Factor ◽  
Data Sets ◽  
State Recognition ◽  
Recognition Model ◽  
Incremental Recognition

Radar working state recognition is the basis of cognitive electronic countermeasures. Aiming at the problem that the traditional supervised recognition technology is difficult to obtain prior information and process the incremental signal data stream, an unsupervised and incremental recognition method is proposed. This method is based on a backpropagation (BP) neural network to construct a recognition model. Firstly, the particle swarm optimization (PSO) algorithm is used to optimize the preference parameter and damping factor of affinity propagation (AP) clustering. Then, the PSO-AP algorithm is used to cluster unlabeled samples to obtain the best initial clustering results. The clustering results are input as training samples into the BP neural network to train the recognition model, which realizes the unsupervised recognition. Secondly, the incremental AP (IAP) algorithm based on the K -nearest neighbor (KNN) idea is used to divide the incremental samples by calculating the closeness between samples. The incremental samples are added to the BP recognition model as a new known state to complete the model update, which realizes incremental recognition. The simulation experiments on three types of radar data sets show that the recognition accuracy of the proposed model can reach more than 83%, which verifies the feasibility and effectiveness of the method. In addition, compared with the AP algorithm and K -means algorithm, the improved AP method improves 59.4%, 17.6%, and 53.5% in purity, rand index (RI), and F -measure indexes, respectively, and the running time is at least 34.8% shorter than the AP algorithm. The time of processing incremental data is greatly reduced, and the clustering efficiency is improved. Experimental results show that this method can quickly and accurately identify radar working state and play an important role in giving full play to the adaptability and timeliness of the cognitive electronic countermeasures.

Study on the Identification Method of Tool Wear State Based on BP Neural Network Optimized by Genetic Algorithm

2014 ◽  
Vol 602-605 ◽  
pp. 2458-2461 ◽  
Author(s):  
Zheng Qiang Li ◽  
Peng Nie ◽  
Shu Guo Zhao ◽  
Zhang Shun Ding
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Acoustic Emission ◽  
Tool Wear ◽  
Bp Neural Network ◽  
Solution Space ◽  
Optimal Solutions ◽  
Accurate Identification ◽  
Identification Method ◽  
Singular Spectrum

According to the un-stationary feature of the acoustic emission signals of tool wear, a tool wear state identification method based on genetic algorithm and BP neural network was proposed. The method reconstructed the acoustic emission signals and calculated the singular spectrum. And the feature vectors were reconstructed based on the singular spectrum. BP neural network was optimized by genetic algorithm. The weights of BP neural network and the thresholds were optimized originally to get more optimal solutions in solution space. Then the more optimal solutions were put into BP neural network to identify the tool wear state by the optimized classification machine. The study indicated that this method can make an accurate identification of tool wear state and should be widely used.

The Design of the BP Neural Network Character Recognition in Matlab Environment

2014 ◽  
Vol 1006-1007 ◽  
pp. 1117-1120 ◽  
Author(s):  
Wen Jie Li ◽  
Jie Zhang ◽  
Ke Lun Tian ◽  
Hai Yan Sun
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Character Recognition ◽  
Test Form ◽  
Identification Method ◽  
Specific Implementation ◽  
Implementation Steps

This paper is based on the BP neural network, the identification method of character and the specific implementation steps were designed. Moreover, the method through the test form has been proved. The accuracy of character recognition is higher.

scholarly journals Target Identification Method of Lingwu dates Based on BP Neural Network

2014 ◽  
Vol 6 (1) ◽  
pp. 670-675 ◽  
Author(s):  
Chen Fengjun ◽  
Liu Kun ◽  
Zhang Junguo ◽  
Lv Sijun ◽  
Li Wenbin
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Target Identification ◽  
Identification Method
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