Information-Applied Technology in BP Neural Network Regression Algorithm with Feature Extraction Using Partial Least Squares

2014 ◽  
Vol 952 ◽  
pp. 299-302
Author(s):  
Xiao Hua Zhang ◽  
Hua Ping Li ◽  
Ke Qiao
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Least Squares ◽  
Partial Least Squares ◽  
Bp Neural Network ◽  
Hybrid Modeling ◽  
The Neural Network ◽  
Applied Technology ◽  
Engineering Materials ◽  
Modeling Algorithm

A hybrid modeling algorithm based on partial least squares and neural network (BP algorithm) is proposed. First it extracts the feature from the original sample sets by partial least squares mehtod, and then the neural network regression using the extraction sets obtained is performed. Thus the hybrid modeling algorithm has the ability of feature extraction. The experiments results on the properties of engineering materials shows that the proposed hybrid algorithm can effectively modeling the properties of engineering materials with merits of dimensions reduction, elimination of noise and multiple correlations between independent variables.

Research on Relevance Vector Machine Model Based on Kernel Partial Least Square in Properties of Engineering Materials

2014 ◽  
Vol 952 ◽  
pp. 311-314
Author(s):  
Xu Sheng Gan ◽  
Can Yang ◽  
Hai Long Gao
Keyword(s):  
Feature Extraction ◽  
Least Squares ◽  
Partial Least Squares ◽  
Relevance Vector Machine ◽  
Partial Least Square ◽  
Least Square ◽  
Machine Model ◽  
Prediction Of Properties ◽  
Kernel Partial Least Squares ◽  
Engineering Materials

To improve the prediction of properties of engineering materials, a Relevance Vector Machine (RVM) regression algorithm based on Kernel Partial Least Squares (KPLS) is proposed. In the algorithm, firstly execute the feature extraction from the original samples using KPLS, and then use obtained feature to realize RVM regression. The simulation shows that the hybrid regression algorithm can effectively reduce the difficulty on RVM modeling and has a wide application in prediction of properties of engineering materials.

A Hybrid Modeling Technique for Partially-Known Systems Using Linear Regression and Neural Network

2009 ◽  
Author(s):  
Andrew J. Joslin ◽  
Chengying Xu
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Least Squares ◽  
Process Model ◽  
Modeling Method ◽  
Hybrid Modeling ◽  
Model Description ◽  
Least Squares Regression ◽  
The Neural Network ◽  
Macro Scale

In this paper a hybrid modeling and system identification method, combining linear least squares regression and artificial neural network techniques, is presented to model a type of dynamic systems which have an incomplete analytical model description. This approach in modeling nonlinear, partially-understood systems is particularly useful to the study of manufacturing processes, where the linear regression portion of the hybrid model is established using a known mathematical model for the process and the neural network is constructed using the residuals from the least squares regression, therefore ensuring a more precise process model for the specific machining setup, tooling selection, workpiece properties, etc. In this paper the method is mathematically proven to give regression coefficients close to those which would be found if only a regression had been performed. The modeling method is then simulated for a macro-scale hard turning process, and the result proves the effectiveness of the proposed hybrid modeling method.

Wavelet Neural Network with PLS Feature Extraction and its Application in Aerodynamic Modeling for Aircraft Stall

2014 ◽  
Vol 602-605 ◽  
pp. 3131-3134
Author(s):  
Xu Sheng Gan ◽  
Xue Qin Tang ◽  
Hai Long Gao
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Least Squares ◽  
Partial Least Squares ◽  
Test Data ◽  
Flight Test ◽  
Data Simulation ◽  
Aerodynamic Modeling ◽  
Aerodynamic Model ◽  
Simulation Results

An aerodynamic modeling method based on WNN with Partial Least Squares (PLS) is proposed. In the method, PLS first is applied to extract the feature of original aerodynamic data samples, and then the obtained feature is used to establish the WNN aerodynamic model for aircraft stall from flight test data. Simulation results are given to illustrate that the proposed method is effective and feasible.

scholarly journals Prediction of ferric iron precipitation in bioleaching process using partial least squares and artificial neural network

2013 ◽  
Vol 19 (3) ◽  
pp. 321-331 ◽  
Author(s):  
Hassan Golmohammadi ◽  
Abbas Rashidi ◽  
Seyed Safdari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Least Squares ◽  
Partial Least Squares ◽  
Ferric Iron ◽  
Structure Property ◽  
Back Propagation Algorithm ◽  
Iron Precipitation ◽  
The Neural Network ◽  
Artificial Neural

A quantitative structure-property relationship (QSPR) study based on partial least squares (PLS) and artificial neural network (ANN) was developed for the prediction of ferric iron precipitation in bioleaching process. The leaching temperature, initial pH, oxidation/reduction potential (ORP), ferrous concentration and particle size of ore were used as inputs to the network. The output of the model was ferric iron precipitation. The optimal condition of the neural network was obtained by adjusting various parameters by trial-and-error. After optimization and training of the network according to back-propagation algorithm, a 5-5-1 neural network was generated for prediction of ferric iron precipitation. The root mean square error for the neural network calculated ferric iron precipitation for training, prediction and validation set are 32.860, 40.739 and 35.890, respectively, which are smaller than those obtained by PLS model (180.972, 165.047 and 149.950, respectively). Results obtained reveal the reliability and good predictivity of neural network model for the prediction of ferric iron precipitation in bioleaching process.

Building Electronic Commerce Recommendation System Based on Ontology Learning and BP Neural Network

2013 ◽  
Vol 718-720 ◽  
pp. 1961-1966
Author(s):  
Hong Sheng Xu ◽  
Qing Tan
Keyword(s):  
Neural Network ◽  
Electronic Commerce ◽  
Bp Neural Network ◽  
Optimization Problem ◽  
Recommendation System ◽  
High Efficiency ◽  
Ontology Learning ◽  
The Neural Network ◽  
Network Process ◽  
Hidden Layer

Electronic commerce recommendation system can effectively retain user, prevent users from erosion, and improve e-commerce system sales. BP neural network using iterative operation, solving the weights of the neural network and close values to corresponding network process of learning and memory, to join the hidden layer nodes of the optimization problem of adjustable parameters increase. Ontology learning is the use of machine learning and statistical techniques, with automatic or semi-automatic way, from the existing data resources and obtaining desired body. The paper presents building electronic commerce recommendation system based on ontology learning and BP neural network. Experimental results show that the proposed algorithm has high efficiency.

The Research on Recognition of the Irregular Objects

2012 ◽  
Vol 241-244 ◽  
pp. 1602-1607
Author(s):  
Guang Hai Han ◽  
Xin Jun Ma
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Convergence Speed ◽  
Know How ◽  
The Neural Network ◽  
Variable Step ◽  
Adaptive Momentum ◽  
The Way

It usually need different ways to process different objects in the manufacturing, Therefore, firstly we need to distinguish the categories of objects to be processed, then the machine will know how to deal with the objects. In order to automatically recognize the category of the irregular object, this paper extracted the improved Hu's moments of each object as the feature by the way of processing images of the working platform that the irregular objects are putting on. This paper adopts the variable step BP neural network with adaptive momentum factor as the classifier. The experiment shows that this method can effectively distinguish different irregular objects, and during the training of the neural network, it has faster convergence speed and better approximation compared with the traditional BP neural network

scholarly journals Research on GDP Forecast Analysis Combining BP Neural Network and ARIMA Model

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shaobo Lu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Error Correction ◽  
Relative Error ◽  
Bp Neural Network ◽  
Arima Model ◽  
Forecast Model ◽  
Price Prediction ◽  
The Neural Network

Based on the BP neural network and the ARIMA model, this paper predicts the nonlinear residual of GDP and adds the predicted values of the two models to obtain the final predicted value of the model. First, the focus is on the ARMA model in the univariate time series. However, in real life, forecasts are often affected by many factors, so the following introduces the ARIMAX model in the multivariate time series. In the prediction process, the network structure and various parameters of the neural network are not given in a systematic way, so the operation of the neural network is affected by many factors. Each forecasting method has its scope of application and also has its own weaknesses caused by the characteristics of its own model. Secondly, this paper proposes an effective combination method according to the GDP characteristics and builds an improved algorithm BP neural network price prediction model, the research on the combination of GDP prediction model is currently mostly focused on the weighted form, and this article proposes another combination, namely, error correction. According to the price characteristics, we determine the appropriate number of hidden layer nodes and build a BP neural network price prediction model based on the improved algorithm. Validation of examples shows that the error-corrected GDP forecast model is also better than the weighted GDP forecast model, which shows that error correction is also a better combination of forecasting methods. The forecast results of BP neural network have lower errors and monthly prices. The relative error of prediction is about 2.5%. Through comparison with the prediction results of the ARIMA model, in the daily price prediction, the relative error of the BP neural network prediction is 1.5%, which is lower than the relative error of the ARIMA model of 2%.

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