PREDICTION OF THE GLASS TRANSITION TEMPERATURES FOR POLYMERS WITH ARTIFICIAL NEURAL NETWORK

2008 ◽  
Vol 07 (05) ◽  
pp. 953-963 ◽  
Author(s):  
XIN-LIANG YU ◽  
BING YI ◽  
XUE-YE WANG
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Glass Transition ◽  
Bp Neural Network ◽  
Linear Regression Analysis ◽  
Back Propagation ◽  
Structure Property ◽  
Qspr Model ◽  
Rms Error ◽  
Artificial Neural

The glass transition temperature (Tg) values of three classes of vinyl polymers, i.e. polystyrenes, polyacrylates, and polymethacrylates, were predicted by using a quantitative structure–property relationship (QSPR) model constructed by back-propagation (BP) neural network. The four descriptors (the rigidness descriptor R HR resulted by hydrogen-bonding moieties group and/or rings, the chain mobility n, the molecular average polarizability α, the net charge of the most negative atom q-) were obtained directly from the polymers' monomer structures. Stepwise multiple linear regression analysis (MLRA) and artificial neural network (ANN) were used to generate the model. Simulated with the final optimum BP neural network [4–2–1], the results showed that the predicted Tg values were in good agreement with the experimental data, with a training set root-mean-square (rms) error of 20.478 K (R = 0.955) and a prediction set rms error of 20.174 K (R = 0.955).

QSPR Studies on n-Octanol/Water Partition Coefficient of Polychlorinated Biphenyls by Using Artificial Neural Network

2012 ◽  
Vol 455-456 ◽  
pp. 925-929
Author(s):  
Long Jiao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Polychlorinated Biphenyls ◽  
Partition Coefficient ◽  
Quantitative Relationship ◽  
Calibration Model ◽  
Structure Property ◽  
Qspr Model ◽  
Water Partition Coefficient ◽  
Artificial Neural

Quantitative structure property relationship (QSPR) model for predicting the n-octanol/water partition coefficient, Kow, of 21 polychlorinated biphenyls (PCBs) was investigated. The structure of the investigated PCBs is mathematically characterized by using molecular distance-edge vector (MDEV) index, a topological index which is developed based on the topological method. The calibration model of Kow was developed by using radial basis function artificial neural network (RBF ANN). Leave one out cross validation was carried out to assess the predictive ability of the developed QSPR model. The R2 between the predicted and experimental logKow is 0.9793. The prediction RMS%RE for the 21 PCBs is 1.92. It is demonstrated that there is a quantitative relationship between the MDEV index and the Kow of the 21 PCBs. RBF ANN is shown to practicable for developing the QSPR model for Kow of PCBs.

Prediction of the Cutting Depth of Abrasive Suspension Jet Using a BP Artificial Neural Network

2012 ◽  
Vol 500 ◽  
pp. 249-252 ◽  
Author(s):  
Xiao Jian Liu ◽  
Qian Qian Fan ◽  
Yan Xia Feng
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Cutting Depth ◽  
Bp Artificial Neural Network ◽  
The Mean ◽  
Artificial Neural ◽  
Model Training ◽  
Abrasive Suspension

Abrasive suspension jet is a new embranchment of abrasive jet. In the cutting process of this jet, the suspension concentration is constant, so the cutting quality is more stable. In this paper, a prediction model based on a back-propagation (BP) artificial neural network is presented for predicting the cutting depth generated by abrasive suspension jet. In the application of the BP neural network, the mean error of the output in the model training is 0.01, the relatively discrepancy is below 8.70%. The modeling method based on the BP neural network is much more convenient and exact compared with traditional methods, and can always achieve a much better prediction effect. It is verified with experiments to be reasonable and feasible, and it is the better foundation for the future study of abrasive suspension jet.

Test on Flood Prediction-Model Using Artificial Neural Network for ShiiLiAn Hydrologic Station on MinChiang,China

2010 ◽  
Vol 39 ◽  
pp. 555-561 ◽  
Author(s):  
Qing Hua Luan ◽  
Yao Cheng ◽  
Zha Xin Ima
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flood Control ◽  
Back Propagation ◽  
Forecast Model ◽  
Flood Forecast ◽  
Back Propagation Algorithm ◽  
Nonlinear Method ◽  
Flood Prediction ◽  
Artificial Neural

The establishing of a precise simulation model for runoff prediction in river with several tributaries is the difficulty of flood forecast, which is also one of the difficulties in hydrologic research. Due to the theory of Artificial Neural Network, using Back Propagation algorithm, the flood forecast model for ShiLiAn hydrologic station in Minjiang River is constructed and validated in this study. Through test, the result shows that the forecast accuracy is satisfied for all check standards of flood forecast and then proves the feasibility of using nonlinear method for flood forecast. This study provides a new method and reference for flood control and water resources management in the local region.

Based on BP Neural Network Discrete Data Forecast

2011 ◽  
Vol 50-51 ◽  
pp. 977-981 ◽  
Author(s):  
Jing Wang ◽  
Guo Li Wang ◽  
Jian Hui Wu ◽  
Yu Su
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Statistical Methods ◽  
Bp Neural Network ◽  
Model Fitting ◽  
Predictive Ability ◽  
Discrete Data ◽  
Engineering System ◽  
Forecast Performance ◽  
Artificial Neural

Artificial neural network is based on human brain structure and operational mechanism based on knowledge and understanding of its structure and behavior of simulated an engineering system. BP artificial neural network is an important component of neural networks, as it can on the linear or nonlinear multivariable without preconditions in the case of statistical analysis, with the traditional statistical methods, analysis of the variables need to be consistent with certain conditions compared to its own advantage. The BP neural network does not need the precise mathematical model, does not have any supposition request to the material itself. Its processing non-linear problem's ability is stronger than traditional statistical methods. This article uses two groups of data to establish the BP neural network model separately, and carries on the comparison to the model fitting ability and the forecast performance, discovered BP neural network when data distribution relative centralism fits ability, forecasts the stable property. But the predictive ability is unable in the discrete data application to achieve anticipated ideally.

QSPR MODELLING OF STABILITY CONSTANTS OF METAL-THIOSEMICARBAZONE COMPLEXES USING MULTIVARIATE REGRESSION METHODS AND ARTIFICIAL NEURAL NETWORK

2021 ◽  
Vol 36 (06) ◽  
Author(s):  
NGUYEN MINH QUANG ◽  
TRAN NGUYEN MINH AN ◽  
NGUYEN HOANG MINH ◽  
TRAN XUAN MAU ◽  
PHAM VAN TAT
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stability Constants ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Structure Property ◽  
Least Square Regression ◽  
Thiosemicarbazone Complexes ◽  
Artificial Neural

In this study, the stability constants of metal-thiosemicarbazone complexes, logb11 were determined by using the quantitative structure property relationship (QSPR) models. The molecular descriptors, physicochemical and quantum descriptors of complexes were generated from molecular geometric structure and semi-empirical quantum calculation PM7 and PM7/sparkle. The QSPR models were built by using the ordinary least square regression (QSPROLS), partial least square regression (QSPRPLS), primary component regression (QSPRPCR) and artificial neural network (QSPRANN). The best linear model QSPROLS (with k of 9) involves descriptors C5, xp9, electric energy, cosmo volume, N4, SsssN, cosmo area, xp10 and core-core repulsion. The QSPRPLS, QSPR PCR and QSPRANN models were developed basing on 9 varibles of the QSPROLS model. The quality of the QSPR models were validated by the statistical values; The QSPROLS: R2train = 0.944, Q2LOO = 0.903 and MSE = 1.035; The QSPRPLS: R2train = 0.929, R2CV = 0.938 and MSE = 1.115; The QSPRPCR: R2train = 0.934, R2CV = 0.9485 and MSE = 1.147. The neural network model QSPRANN with architecture I(9)-HL(12)-O(1) was presented also with the statistical values: R2train = 0.9723, and R2CV = 0.9731. The QSPR models also were evaluated externally and got good performance results with those from the experimental literature.

Non-invasive prediction of bloodstain age using the principal component and a back propagation artificial neural network

2017 ◽  
Vol 14 (9) ◽  
pp. 095601 ◽  
Author(s):  
Huimin Sun ◽  
Yaoyong Meng ◽  
Pingli Zhang ◽  
Yajing Li ◽  
Nan Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Principal Component ◽  
Non Invasive ◽  
Artificial Neural

scholarly journals Rainfall Forecasting Using Various Artificial Neural Network Techniques - A Review

2021 ◽  
pp. 506-526
Author(s):  
Nisha Thakur ◽  
Sanjeev Karmakar ◽  
Sunita Soni
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Moving Average ◽  
Weather Prediction ◽  
Optimization Technique ◽  
Back Propagation ◽  
Rainfall Forecasting ◽  
Rainfall Prediction ◽  
Artificial Neural ◽  
Review Reports

The present review reports the work done by the various authors towards rainfall forecasting using the different techniques within Artificial Neural Network concepts. Back-Propagation, Auto-Regressive Moving Average (ARIMA), ANN , K- Nearest Neighbourhood (K-NN), Hybrid model (Wavelet-ANN), Hybrid Wavelet-NARX model, Rainfall-runoff models, (Two-stage optimization technique), Adaptive Basis Function Neural Network (ABFNN), Multilayer perceptron, etc., algorithms/technologies were reviewed. A tabular representation was used to compare the above-mentioned technologies for rainfall predictions. In most of the articles, training and testing, accuracy was found more than 95%. The rainfall prediction done using the ANN techniques was found much superior to the other techniques like Numerical Weather Prediction (NWP) and Statistical Method because of the non-linear and complex physical conditions affecting the occurrence of rainfall.

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