Seismic signal recognition using improved BP neural network and combined feature extraction method

Peng, Zhaoqin; Cao, Chun; Huang, Jiaoying; Liu, Qiusheng

doi:10.1007/s11771-014-2136-8

Cited by 7 publications

(4 citation statements)

References 23 publications

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“…Peng Zhao-qin, Cao Chun, Huang Jiang-ying, Liu Qiu-Sheng focus on the recognition of seismic signal [6] . For the limit of traditional BP network, they proposed an improved BP model.…”

Section: Related Workmentioning

confidence: 99%

Extended BP Network-Based Pavement and Road Settlement Prediction

Jing

2014

AMM

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In recent years, many approaches were proposed to deal with the settlement prediction problem. However, because of the complexity of geography and other conditions, the current solutions still have some deficiencies and do not work well. To improve the accuracy of settlement prediction, the authors of this paper propose an extended BP network-based approach. This method first designed the model of BP network according to the dataset. This network includes one input layer, one output layer, one middle layer and an extended middle layer. After trained by samples, this network is used to predict the settlement value. The experiment indicates that our approach is available and work well in most situations.

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“…Peng Zhao-qin, Cao Chun, Huang Jiang-ying, Liu Qiu-Sheng focus on the recognition of seismic signal [6] . For the limit of traditional BP network, they proposed an improved BP model.…”

Section: Related Workmentioning

confidence: 99%

Extended BP Network-Based Pavement and Road Settlement Prediction

Jing

2014

AMM

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“…Simulation and experimental results showed that this method has better real-time accuracy for gesture recognition. Peng et al [4] proposed a vehicle and personnel identification method based on an improved neural network-that is, the seismic signals of moving objects are processed and analyzed to obtain eigenvectors-so as to realize good classification. Experimental results showed that the method has better recognition accuracy and effectiveness.…”

Section: Introductionmentioning

confidence: 99%

Research on the Prediction Method of Centrifugal Pump Performance Based on a Double Hidden Layer BP Neural Network

Han

Nan

et al. 2019

Energies

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With the aim of improving the shortcomings of the traditional single hidden layer back propagation (BP) neural network structure and learning algorithm, this paper proposes a centrifugal pump performance prediction method based on the combination of the Levenberg–Marquardt (LM) training algorithm and double hidden layer BP neural network. MATLAB was used to establish a double hidden layer BP neural network prediction model to predict the head and efficiency of a centrifugal pump. The average relative error of the head between the experimental and prediction obtained by the double hidden layer BP neural network model was 4.35%, the average relative error of the model prediction efficiency and the experimental efficiency was 2.94%, and the convergence time was 1/42 of that of the single hidden layer. The double hidden layer BP neural network model effectively solves the problems of low learning efficiency and easy convergence into local minima—issues that were common in the traditional single hidden layer BP neural network training. Furthermore, the proposed model realizes hydraulic performance prediction during the design process of a centrifugal pump.

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“…Del Pezzo et al [20] used ANN based on seismogram signatures to classify earthquakes and underwater explosions. Peng et al [21] used improved BPNN and combined feature extraction method to recognize seismic signal.…”

Section: Introductionmentioning

confidence: 99%

Discrimination of Rock Fracture and Blast Events Based on Signal Complexity and Machine Learning

Zhou

Cheng

Cai

et al. 2018

Shock and Vibration

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The automatic discrimination of rock fracture and blast events is complex and challenging due to the similar waveform characteristics. To solve this problem, a new method based on the signal complexity analysis and machine learning has been proposed in this paper. First, the permutation entropy values of signals at different scale factors are calculated to reflect complexity of signals and constructed into a feature vector set. Secondly, based on the feature vector set, back-propagation neural network (BPNN) as a means of machine learning is applied to establish a discriminator for rock fracture and blast events. Then to evaluate the classification performances of the new method, the classifying accuracies of support vector machine (SVM), naive Bayes classifier, and the new method are compared, and the receiver operating characteristic (ROC) curves are also analyzed. The results show the new method obtains the best classification performances. In addition, the influence of different scale factor q and number of training samples n on discrimination results is discussed. It is found that the classifying accuracy of the new method reaches the highest value when q = 8–15 or 8–20 and n=140.

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Seismic signal recognition using improved BP neural network and combined feature extraction method

Cited by 7 publications

References 23 publications

Extended BP Network-Based Pavement and Road Settlement Prediction

Extended BP Network-Based Pavement and Road Settlement Prediction

Research on the Prediction Method of Centrifugal Pump Performance Based on a Double Hidden Layer BP Neural Network

Discrimination of Rock Fracture and Blast Events Based on Signal Complexity and Machine Learning

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