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Simulation of Silty Clay Compressibility Parameters Based on Improved BP Neural Network Using bayesian regularization

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Earthquake Research in China 2020年第3期34卷 378-393页

作者： CAI Run PENG Tao WANG Qian HE Fanmin ZHAO Duoying Chengdu Surveying Geotechnical Research Institute Co Ltd.of MCCChengdu 610023China Key Laboratory of Loess Earthquake Engineering Gansu Earthquake AgencyLanzhou 730000China Lanzhou Institute of Seismology China Earthquake AdministrationLanzhou 730000China

Soil compressibility parameters are important indicators in the geotechnical field and are affected by various factors such as natural conditions and human *** the sample size is too large,conventional methods require massive human and financial *** order to reasonably simulate the compressibility parameters of the sample,this paper firstly adopts the correlation analysis to select seven influencing *** of the factors has a high correlation with compressibility ***,the proportion of the weights of the seven factors in the bayesian neural network is analyzed based on Garson ***,an output model of the compressibility parameters of BR-BP silty clay is established based on bayesian regularized BP neural ***,the model is used to simulate the measured compressibility *** output results are compared with the measured values and the output results of the traditional LM-BP neural *** results show that the model is more stable and has stronger nonlinear fitting *** output of the model is basically consistent with the actual *** with the traditional LMBP neural network model,its data sensitivity is enhanced,and the accuracy of the output result is significantly improved,the average value of the relative error of the compression coefficient is reduced from 15.54%to 6.15%,and the average value of the relative error of the compression modulus is reduced from 6.07%to 4.62%.The results provide a new technical method for obtaining the compressibility parameters of silty clay in this area,showing good theoretical significance and practical value.

关键词： Silty clay Compressibility Correlation analysis bayesian regularization Neural networks

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Performance comparison of training algorithms for the estimation of B?hme abrasion resistance using neural networks

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Journal of Mountain Science 2023年第12期20卷 3732-3742页

作者： Ali Can OZDEMIR Esma KAHRAMAN Department of Mining Engineering Çukurova UniversityAdana 01250Türkiye

Natural stones used as floor and wall coverings are exposed to many different abrasive forces,so it is essential to choose suitable materials for wear resistance in terms of the life of the *** abrasion resistance of natural stones can be determined in the laboratory by applying the B?hme abrasion resistance(BAR)***,the direct analysis of BAR in the laboratory has disadvantages such as wasting time and energy,experimental errors,and health *** eliminate these disadvantages,the estimation of BAR using artificial neural networks(ANN)was *** natural stone samples were collected from Türkiye,and uniaxial compressive strength(UCS),flexural strength(FS),water absorption rate(WA),unit volume weight(UW),effective porosity(n),and BAR tests were carried *** outputs of these tests were gathered and a data set,consisting of a total of 105 data,was randomly divided into two groups:testing and *** the current study,the success of three different training algorithms of Levenberg-Marquardt(LM),bayesian regularization(BR),and scaled conjugate gradient(SCG)were compared for BAR prediction of natural *** criteria such as coefficient of determination(R~2),mean square error(MSE),mean square error(RMSE),and mean absolute percentage error(MAPE),which are widely used and adopted in the literature,were used to determine predictive *** findings of the study indicated that ANN is a valid method for estimating the BAR ***,the LM algorithm(R~2=0.9999,MSE=0.0001,RMSE=0.0110,and MAPE=0.0487)in training and the BR algorithm(R~2=0.9896,MSE=0.0589,RMSE=0.2427,and MAPE=1.2327)in testing showed the best prediction *** has been observed that the proposed method is quite practical to *** the artificial neural networks method will provide an advantage in similar laborintensive experimental studies.

关键词： Böhme abrasion resistance Neural networks Levenberg-Marquardt bayesian regularization Scaled conjugate gradient

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Retrieval Snow Depth by Artificial Neural Network Methodology from Integrated AMSR-E and In-situ Data——A Case Study in Qinghai-Tibet Plateau

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Chinese Geographical Science 2008年第4期18卷 356-360页

作者： CAO Yungang YANG Xiuchun ZHU Xiaohua Department of Survey Engineering School of Civil Engineering Southwest Jiaotong University Chengdu 610031 China Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences Beijing 100081 China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China

On the basis of artificial neural network (ANN) model, this paper presents an algorithm for inversing snow depth with use of AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System (EOS)) dataset, i.e., brightness temperature at 18.7 and 36.5GHz in Qinghai-Tibet Plateau during the snow season of 2002-2003. In order to overcome the overfitting problem in ANN modeling, this methodology adopts a bayesian regularization approach. The experiments are performed to compare the results obtained from the ANN-based algorithm with those obtained from other existing algorithms, i.e., Chang algorithm, spectral polarization difference (SPD) algorithm, and temperature gradient (TG) algorithm. The experimental results show that the presented algorithm has the highest accuracy in estimating snow depth. In addition, the effects of the noises in datasets on model fitting can be decreased due to adopting the bayesian regularization approach.

关键词： artificial neural network bayesian regularization snow depth brightness temperature Qinghai-Tibet Plateau

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Nonlinear diffusion methods based on robust statistics for noise removal

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Journal of Harbin Institute of Technology(New Series) 2007年第3期14卷 440-444页

作者：贾迪野黄凤岗苏菡 School of Computer Science and Technology Harbin Engineering University

A novel smoothness term of bayesian regularization framework based on M-estimation of robust statistics is proposed, and from this term a class of fourth-order nonlinear diffusion methods is proposed. These methods attempt to approximate an observed image with a piecewise linear image, which looks more natural than piecewise constant image used to approximate an observed image by P-M model. It is known that M-estimators and W-estimators are essentially equivalent and solve the same minimization problem. Then, we propose PL bilateral filter from equivalent W-estimator. This new model is designed for piecewise linear image filtering, which is more effective than normal bilateral filter.

关键词： bayesian regularization M-estimation nonlinear diffusion bilateral filter

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Indian stock market prediction using artificial neural networks on tick data

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Financial Innovation 2019年第1期5卷 267-278页

作者： Dharmaraja Selvamuthu Vineet Kumar Abhishek Mishra Department of Mathematics Indian Institute of Technology DelhiHauz KhasNew Delhi 110016India

Introduction:Nowadays,the most significant challenges in the stock market is to predict the stock *** stock price data represents a financial time series data which becomes more difficult to predict due to its characteristics and dynamic *** description:Support Vector Machines(SVM)and Artificial Neural Networks(ANN)are widely used for prediction of stock prices and its *** algorithm has its way of learning patterns and then *** Neural Network(ANN)is a popular method which also incorporate technical analysis for making predictions in financial *** and evaluation:Most common techniques used in the forecasting of financial time series are Support Vector Machine(SVM),Support Vector Regression(SVR)and Back Propagation Neural Network(BPNN).In this article,we use neural networks based on three different learning algorithms,i.e.,Levenberg-Marquardt,Scaled Conjugate Gradient and bayesian regularization for stock market prediction based on tick data as well as 15-min data of an Indian company and their results ***:All three algorithms provide an accuracy of 99.9%using tick *** accuracy over 15-min dataset drops to 96.2%,97.0%and 98.9%for LM,SCG and bayesian regularization respectively which is significantly poor in comparison with that of results obtained using tick data.

关键词： Neural Networks Indian Stock Market Prediction Levenberg-Marquardt Scale Conjugate Gradient bayesian regularization Tick by tick data

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A Novel Method for Nonlinear Time Series Forecasting of Time-Delay Neural Network

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Wuhan University Journal of Natural Sciences 2006年第5期11卷 1357-1361页

作者： JIANG Weijin XU Yuhui Department of Computer Hunan University of TechnologyZhuzhou 412008 Hunan China Department ot Information and Computer Hunan University ofTechnology Zhuzhou 412008 Hunan China

Based on the idea of nonlinear prediction of phase space reconstruction, this paper presented a time delay BP neural network model, whose generalization capability was improved by bayesian regularization. Furthermore, the model is applied to forecast the import and export trades in one industry. The results showed that the improved model has excellent generalization capabilities, which not only learned the historical curve, but efficiently predicted the trend of business. Comparing with common evaluation of forecasts, we put on a conclusion that nonlinear forecast can not only focus on data combination and precision improvement, it also can vividly reflect the nonlinear characteristic of the forecas ting system. While analyzing the forecasting precision of the model, we give a model judgment by calculating the nonlinear characteristic value of the combined serial and original serial, proved that the forecasting model can reasonably catch＇ the dynamic characteristic of the nonlinear system which produced the origin serial.

关键词： nonlinear prediction phase space reconstruction BP bayesian regularization

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Using Radial Neural Network to Predict the Ultimate Moment of a Reinforced Concrete Beam Reinforced with Composites

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Open Journal of Civil Engineering 2022年第3期12卷 353-369页

作者： Santatra Mitsinjo Randrianarisoa Lydie Chantale Andriambahoaka Herimiah Stelarijao Rakotondranja Andrianary Lala Raminosoa Université d’Antananarivo Antananarivo Madagascar

This article is intended as a proposal for a numerical model for the prediction of the ultimate moment of a reinforced concrete beam reinforced with composite materials based on neural networks, which are classified in the artificial intelligence method. In this work, a RBF network or radial basis function type model was created and tested. The validation of the RBF architecture consists in judging its predictive capacity by using the weights and biases computed during the training, to apply them to another database which did not participate to the training and testing of the model. So, with bayesian regularization, a maximum error of 0.0813 Tm in absolute value was found between the targets and predicted outputs. The value of the mean square error MSE = 1.1106 * 10-4 allowed us to quantify and justify the prediction performance of this network. Through this article, RBF network model was justified perform and can be used and exploited by our engineers with a high reliability rate.

关键词： Nash-Sutcliffe Criteria Ultimate Limit State Simple Bending BAEL RBF Neural Network bayesian regularization

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A NEW METHOD TO ACQUIRE MORE ECT PROJECTION DATA BASED ON NEURAL NETWORKS

A NEW METHOD TO ACQUIRE MORE ECT PROJECTION DATA BASED ON NE...

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多相流研究进展国际会议

作者： Lijian Yang Chunting Liu Hua Yan School of information Science & Engineering Shenyang University of Technology Shenyang 110023 China

＜正＞ Compared with other industrial tomography modalities, electrical capacitance tomography （ECT） has some advantages: low-cost, fast response and no radiation. But the number of projection data collected is very small compared to the number of pixels required for an acceptable image. Increasing the number of measurement electrode, e.g. form 8 to 16, can acquire more projection data, but needs much higher hardware performance. In this paper, a new method to acquire more projection data to improve reconstruction image quality is proposed. Back-propagation neural networks are used to deduce the projection data of a 16-electrode ECT sensor from the projection data of an 8-electrode ECT sensor. bayesian regularization is used and combined with Levenberg-Marquardt training algorithm to improve the network’s generalization capability and diminish network-training time. Simulation results illustrate that the new method to acquire more projection data proposed in this paper can be used to improve reconstructed image quality.

关键词： capacitance tomography neural network bayesian regularization image reconstruction

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