Intelligent Identification of Flow Regime Based on Improved BP and IMFs

2013 ◽  
Vol 816-817 ◽  
pp. 471-474
Author(s):  
Qiang Wang
Keyword(s):  
Neural Network ◽  
Flow Regime ◽  
Bp Neural Network ◽  
Flow Regimes ◽  
Characteristic Vector ◽  
Soft Sensing ◽  
Mode Decomposition ◽  
Training Samples ◽  
The Difference ◽  
Regime Characteristic

Aimed at the characteristic of nonlinear and non-stationary of pressure drop, in this article a flow regime identification soft sensing method using Hilbert-Huang transformation combined with improved BP neural network is put forward. The method analyzes the intrinsic mode function (IMFs) obtained after the empirical mode decomposition (EMD), then extracts IMF energy as the characteristic vector of an improved BP neural network with self-adapted learning ratio. Learning form training samples, the network could accomplish the objective identification of the unknown flow regimes. The simulated results showed that the flow regime characteristic vector which was obtained by IMFs could reflect the difference between various flow regimes and the recognition possibility of the network could reached up to about 95 percent. This study provided a new way to identify flow regime by soft sensing.

Research on Roller Bearing with Fault Diagnosis Method Based on EMD and BP Neural Network

2014 ◽  
Vol 1014 ◽  
pp. 501-504 ◽  
Author(s):  
Shu Guo ◽  
You Cai Xu ◽  
Xin Shi Li ◽  
Ran Tao ◽  
Kun Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Classification Performance ◽  
Outer Race ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Main Fault

In order to discover the fault with roller bearing in time, a new fault diagnosis method based on Empirical mode decomposition (EMD) and BP neural network is put forward in the paper. First, we get the fault signal through experiments. Then we use EMD to decompose the vibration signal into a series of single signals. We can extract main fault information from the single signals. The kurtosis coefficient of the single signals forms a feature vector which is used as the input data of the BP neural network. The trained BP neural network can be used for fault identification. Through analyzing, BP neural network can distinguish the fault into normal state, inner race fault, outer race fault. The results show that this method can gain very stable classification performance and good computational efficiency.

Prediction and analysis of College Students’ mental health based on BP neural network

2020 ◽  
pp. 1-7
Author(s):  
Tang Yushou Su Jianhuan
Keyword(s):  
Neural Network ◽  
Mental Health ◽  
College Students ◽  
Bp Neural Network ◽  
Structural Characteristics ◽  
Prediction System ◽  
The Neural Network ◽  
Data Processing Method ◽  
Training Samples ◽  
The Mathematical Model

College Students’ mental health is an important part of higher education, so the current research and prediction of College Students’ mental health are of great significance to better solve the problem of College Students’ mental health. Taking a local university as an example, the data from 2011 to 2019 are selected and analyzed. The normalized data processing method is used to assign weights to 11 kinds of factors that affect the health of college students. The training samples of a neural network are selected, and the structural characteristics of the neural network and the artificial neural network toolbox of MATLAB are used to establish the BP based model the mathematical model of the prediction system of College Students’ mental health based on neural network. The results show that the error between the predicted value and the measured value is only 0.88%. On this basis, this paper uses the model to predict the weight of the influencing factors of the mental health status of college students in a local university in 2020 and analyzes the causes of the prediction results, to provide the basis for the current mental health education of college students.

Image Segmentation of Eggplants in Growing Environment Based on Improved BP Neural Network

2010 ◽  
Vol 40-41 ◽  
pp. 599-603
Author(s):  
Jian Song
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Bp Neural Network ◽  
Image Features ◽  
The Neural Network ◽  
Training Samples ◽  
Neural Network Algorithm ◽  
Growing Environment ◽  
Improved Bp Algorithm ◽  
Gray Values

Aim at the complex background of eggplant image in the growing environment, a image segmentation method based on BP neural network was put forward. The EXG gray values of 3×3 neighborhood pixels were obtained as image features through by analyzing the eggplant image. 30 eggplant images were taken as training samples and results of manual segmentation images by Photoshop were regarded as teacher signals. The improved BP algorithm was used to train the parameter of the neural network. The effective parameter was achieved after 120 times of training. The result of this experiment showed that the eggplant fruit could be preferably segmented from the background by using BP neural network algorithm and it could totally meet the demands of the picking robots after further processing by way of combining mathematics morphology with median filtering.

BP Neural Network Based Animation Production Prediction

2014 ◽  
Vol 539 ◽  
pp. 475-478
Author(s):  
Ran Tao ◽  
Da Chao Yuan ◽  
Gang Yi Hu
Keyword(s):  
Neural Network ◽  
Relative Error ◽  
Bp Neural Network ◽  
Prediction Method ◽  
Coefficient Of Determination ◽  
Mean Relative Error ◽  
Training Samples ◽  
The Mean ◽  
Production Prediction

In order to research the basic condition of animation production, this article chooses BP Neural Network to predict the animation production. We select 13 test samples, selected nine of them randomly as training samples, and the remaining four as the test samples. The coefficient of determination is 0.99839 and the mean relative error is 0.186125. The result shows that BP Neural Network is an effective prediction method.

scholarly journals Identification betweenStephania tetrandraS. Moore andStephania cepharanthaHayata by CWT–FTIR–RBFNN

2008 ◽  
Vol 22 (5) ◽  
pp. 371-386 ◽  
Author(s):  
Chang-Jiang Zhang ◽  
Cun-Gui Cheng
Keyword(s):  
Neural Network ◽  
Feature Vector ◽  
Recognition Rate ◽  
Total Reflection ◽  
Chemical Components ◽  
Continuous Wavelet ◽  
Feature Extracting ◽  
Training Samples ◽  
The Difference ◽  
Stephania Tetrandra

Horizontal attenuation total reflection–Fourier transform infrared spectroscopy (HATR–FTIR) is used to measure the FTIR ofStephania tetrandraS. Moore andStephania cepharanthaHayata. Because they belong to the same family and the same genus Chinese traditional medicinal materials, their chemical components are very similar. In order to extrude the difference betweenStephania tetrandraS. Moore andStephania cepharanthaHayata, continuous wavelet transform (CWT) is used to decompose the FTIR ofStephania tetrandraS. Moore andStephania cepharanthaHayata. Three main scales are selected as the feature extracting space in the CWT domain. According the distribution of FTIR ofStephania tetrandraS. Moore andStephania cepharanthaHayata, three feature regions are determined at every spectra band at selected three scales in the CWT domain. Thus nine feature parameters form the feature vector. The feature vector is input to the radius basis function neural network (RBFNN) to train so as to accurately classify theStephania tetrandraS. Moore andStephania cepharanthaHayata. 128 couples of FTIR are used to train and test the proposed method, where 78 couples of data are used as training samples and 50 couples of data are used as testing samples. Experimental results show that the accurate recognition rate betweenStephania tetrandraS. Moore andStephania cepharanthaHayata is respectively 99.8 and 99.9% by using the proposed method.

scholarly journals Recognition and Classification of Incipient Cable Failures Based on Variational Mode Decomposition and a Convolutional Neural Network

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 2005 ◽  
Author(s):  
Jiaying Deng ◽  
Wenhai Zhang ◽  
Xiaomei Yang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Bp Neural Network ◽  
Nearest Neighbor ◽  
Feature Vector ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
K Nearest Neighbor ◽  
Mode Decomposition

To avoid power supply hazards caused by cable failures, this paper presents an approach of incipient cable failure recognition and classification based on variational mode decomposition (VMD) and a convolutional neural network (CNN). By using VMD, the original current signal is decomposed into seven modes with different center frequencies. Then, 42 features are extracted for the seven modes and used to construct a feature vector as input of the CNN to classify incipient cable failure through deep learning. Compared with using the original signals directly as the CNN input, the proposed approach is more efficient and robust. Experiments on different classifiers, namely, the decision tree (DT), K-nearest neighbor (KNN), BP neural network (BP) and support vector machine (SVM), and show that the CNN outperforms the other classifiers in terms of accuracy.

scholarly journals A Hybrid Model for Forecasting Sunspots Time Series Based on Variational Mode Decomposition and Backpropagation Neural Network Improved by Firefly Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Guohui Li ◽  
Xiao Ma ◽  
Hong Yang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hybrid Model ◽  
Bp Neural Network ◽  
Firefly Algorithm ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Earth’S Climate ◽  
Bp Model

The change of the number of sunspots has a great impact on the Earth’s climate, agriculture, communications, natural disasters, and other aspects, so it is very important to predict the number of sunspots. Aiming at the chaotic characteristics of monthly mean of sunspots, a novel hybrid model for forecasting sunspots time-series based on variational mode decomposition (VMD) and backpropagation (BP) neural network improved by firefly algorithm (FA) is proposed. Firstly, a set of intrinsic mode functions (IMFs) are obtained by VMD decomposition of the monthly mean time series of the sunspots. Secondly, the firefly algorithm is introduced to initialize the weights and thresholds of the BP neural network, and a prediction model is established for each IMF. Finally, the predicted values of these components are calculated to obtain the final predict results. Comparing BP model, FA-BP model, EMD-BP model, and VMD-BP model, the simulation results show that the proposed algorithm has higher prediction accuracy and can be used to forecast the time series of sunspots.

scholarly journals Hybrid improved empirical mode decomposition and BP neural network model for the prediction of sea surface temperature

Ocean Science ◽  
2019 ◽  
Vol 15 (2) ◽  
pp. 349-360 ◽  
Author(s):  
Zhiyuan Wu ◽  
Changbo Jiang ◽  
Mack Conde ◽  
Bin Deng ◽  
Jie Chen
Keyword(s):  
Neural Network ◽  
Empirical Mode Decomposition ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Ensemble Empirical Mode Decomposition ◽  
The North ◽  
Mode Decomposition ◽  
Bpnn Model

Abstract. Sea surface temperature (SST) is the major factor that affects the ocean–atmosphere interaction, and in turn the accurate prediction of SST is the key to ocean dynamic prediction. In this paper, an SST-predicting method based on empirical mode decomposition (EMD) algorithms and back-propagation neural network (BPNN) is proposed. Two different EMD algorithms have been applied extensively for analyzing time-series SST data and some nonlinear stochastic signals. The ensemble empirical mode decomposition (EEMD) algorithm and complementary ensemble empirical mode decomposition (CEEMD) algorithm are two improved algorithms of EMD, which can effectively handle the mode-mixing problem and decompose the original data into more stationary signals with different frequencies. Each intrinsic mode function (IMF) has been taken as input data to the back-propagation neural network model. The final predicted SST data are obtained by aggregating the predicted data of individual series of IMFs (IMFi). A case study of the monthly mean SST anomaly (SSTA) in the northeastern region of the North Pacific shows that the proposed hybrid CEEMD-BPNN model is much more accurate than the hybrid EEMD-BPNN model, and the prediction accuracy based on a BP neural network is improved by the CEEMD method. Statistical analysis of the case study demonstrates that applying the proposed hybrid CEEMD-BPNN model is effective for the SST prediction. Highlights include the following: Highlights. An SST-predicting method based on the hybrid EMD algorithms and BP neural network method is proposed in this paper. SST prediction results based on the hybrid EEMD-BPNN and CEEMD-BPNN models are compared and discussed. A case study of SST in the North Pacific shows that the proposed hybrid CEEMD-BPNN model can effectively predict the time-series SST.

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