Estimation of Polypropylene Melt Index through the Application of Stacked Neural Networks

2011 ◽  
Vol 187 ◽  
pp. 411-415
Author(s):  
Lu Yue Xia ◽  
Hai Tian Pan ◽  
Meng Fei Zhou ◽  
Yi Jun Cai ◽  
Xiao Fang Sun
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Model Performance ◽  
Measurement Interval ◽  
Melt Index ◽  
Polypropylene Melt ◽  
Single Neural Network

Melt index is the most important parameter in determining the polypropylene grade. Since the lack of proper on-line instruments, its measurement interval and delay are both very long. This makes the quality control quite difficult. A modeling approach based on stacked neural networks is proposed to estimation the polypropylene melt index. Single neural network model generalization capability can be significantly improved by using stacked neural networks model. Proper determination of the stacking weights is essential for good stacked neural networks model performance, so determination of appropriate weights for combining individual networks using the criteria about minimization of sum of absolute prediction error is proposed. Application to real industrial data demonstrates that the polypropylene melt index can be successfully estimated using stacked neural networks. The results obtained demonstrate significant improvements in model accuracy, as a result of using stacked neural networks model, compared to using single neural network model.

Modeling Methodology Based on Stacked Neural Networks for Inferential Prediction of Polypropylene Melt Index

2011 ◽  
Vol 55-57 ◽  
pp. 670-674
Author(s):  
Lu Yue Xia ◽  
Hai Tian Pan ◽  
Meng Fei Zhou ◽  
Yi Jun Cai ◽  
Xiao Fang Sun
Keyword(s):  
Neural Networks ◽  
Model Performance ◽  
Propylene Polymerization ◽  
Melt Index ◽  
Modeling Methodology ◽  
Data Points ◽  
Robust Least Squares ◽  
Polypropylene Melt ◽  
Characteristic Process

A modeling methodology based on stacked neural networks by combining several individual networks in parallel is proposed. Stacked neural network as an effective method for modeling of inherently complex and nonlinear systems especially a system with a limited number of experimental data points is chosen for yield prediction. Proper determination of the stacking weights is essential for good stacked neural networks model performance, so determination of appropriate weights for combining individual networks using robust least squares estimation is proposed. Inferential prediction of melt index as the most important characteristic process of polypropylene polymerization has been carried out. The application of the proposed modeling method based on stacked neural networks to the development of melt index soft sensor in an industrial propylene polymerization plant demonstrates its effectiveness.

scholarly journals Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Petr Maca ◽  
Pavel Pech
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Multilayer Perceptron ◽  
Standardized Precipitation Index ◽  
Model Performance ◽  
Drought Indices ◽  
Artificial Neural

The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI) and the standardized precipitation evaporation index (SPEI) and were derived for the period of 1948–2002 on two US catchments. The meteorological and hydrological data were obtained from MOPEX experiment. The training of both neural network models was made by the adaptive version of differential evolution, JADE. The comparison of models was based on six model performance measures. The results of drought indices forecast, explained by the values of four model performance indices, show that the integrated neural network model was superior to the feedforward multilayer perceptron with one hidden layer of neurons.

scholarly journals Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia

2012 ◽  
Vol 16 (4) ◽  
pp. 1151-1169 ◽  
Author(s):  
A. El-Shafie ◽  
A. Noureldin ◽  
M. Taha ◽  
A. Hussain ◽  
M. Mukhlisin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Network Architecture ◽  
Rainfall Time Series ◽  
Hydrological Process ◽  
Multi Layer Perceptron ◽  
Rainfall Forecasting ◽  
Unseen Data

Abstract. Rainfall is considered as one of the major components of the hydrological process; it takes significant part in evaluating drought and flooding events. Therefore, it is important to have an accurate model for rainfall forecasting. Recently, several data-driven modeling approaches have been investigated to perform such forecasting tasks as multi-layer perceptron neural networks (MLP-NN). In fact, the rainfall time series modeling involves an important temporal dimension. On the other hand, the classical MLP-NN is a static and has a memoryless network architecture that is effective for complex nonlinear static mapping. This research focuses on investigating the potential of introducing a neural network that could address the temporal relationships of the rainfall series. Two different static neural networks and one dynamic neural network, namely the multi-layer perceptron neural network (MLP-NN), radial basis function neural network (RBFNN) and input delay neural network (IDNN), respectively, have been examined in this study. Those models had been developed for the two time horizons for monthly and weekly rainfall forecasting at Klang River, Malaysia. Data collected over 12 yr (1997–2008) on a weekly basis and 22 yr (1987–2008) on a monthly basis were used to develop and examine the performance of the proposed models. Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static and dynamic neural networks. Results showed that the MLP-NN neural network model is able to follow trends of the actual rainfall, however, not very accurately. RBFNN model achieved better accuracy than the MLP-NN model. Moreover, the forecasting accuracy of the IDNN model was better than that of static network during both training and testing stages, which proves a consistent level of accuracy with seen and unseen data.

Credit Scoring Using Supervised and Unsupervised Neural Networks

2002 ◽  
pp. 154-166 ◽  
Author(s):  
David West ◽  
Cornelius Muchineuta
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Support ◽  
Network Model ◽  
Neural Network Model ◽  
Credit Scoring ◽  
Self Organizing Map ◽  
Subprime Lending ◽  
The Neural Network ◽  
Combined Use

Some of the concerns that plague developers of neural network decision support systems include: (a) How do I understand the underlying structure of the problem domain; (b) How can I discover unknown imperfections in the data which might detract from the generalization accuracy of the neural network model; and (c) What variables should I include to obtain the best generalization properties in the neural network model? In this paper we explore the combined use of unsupervised and supervised neural networks to address these concerns. We develop and test a credit-scoring application using a self-organizing map and a multilayered feedforward neural network. The final product is a neural network decision support system that facilitates subprime lending and is flexible and adaptive to the needs of e-commerce applications.

Comparison of LSSVM model results with artificial neural network model for determination of the solubility of SO2 in ionic liquids

2020 ◽  
Vol 304 ◽  
pp. 112771 ◽  
Author(s):  
Hadi Mokarizadeh ◽  
Saeid Atashrouz ◽  
Hamed Mirshekar ◽  
Abdolhossein Hemmati-Sarapardeh ◽  
Ahmad Mohaddes Pour
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ionic Liquids ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Artificial Neural

APPLYING NEURAL NETWORKS TO SOFTWARE RELIABILITY ASSESSMENT

2010 ◽  
Vol 17 (04) ◽  
pp. 313-329 ◽  
Author(s):  
NORMAN SCHNEIDEWIND
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Software Reliability ◽  
Likelihood Estimation ◽  
Time To Failure ◽  
The Neural Network ◽  
Method Of Maximum Likelihood ◽  
Network Method

We adapt concepts from the field of neural networks to assess the reliability of software, employing cumulative failures, reliability, remaining failures, and time to failure metrics. In addition, the risk of not achieving reliability, remaining failure, and time to failure goals are assessed. The purpose of the assessment is to compare a criterion, derived from a neural network model, for estimating the parameters of software reliability metrics, with the method of maximum likelihood estimation. To our surprise the neural network method proved superior for all the reliability metrics that were assessed by virtue of yielding lower prediction error and risk. We also found that considerable adaptation of the neural network model was necessary to be meaningful for our application – only inputs, functions, neurons, weights, activation units, and outputs were required to characterize our application.

DYNAMIC OUTPUT FEEDBACK STABILIZATION FOR NONLINEAR SYSTEMS BASED ON STANDARD NEURAL NETWORK MODELS

2006 ◽  
Vol 16 (04) ◽  
pp. 305-317 ◽  
Author(s):  
MEIQIN LIU
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Nonlinear Systems ◽  
Network Model ◽  
Neural Network Model ◽  
Output Feedback ◽  
Control Design ◽  
Feedback Stabilization ◽  
Dynamic Output Feedback ◽  
Dynamic Output

A neural-model-based control design for some nonlinear systems is addressed. The design approach is to approximate the nonlinear systems with neural networks of which the activation functions satisfy the sector conditions. A novel neural network model termed standard neural network model (SNNM) is advanced for describing this class of approximating neural networks. Full-order dynamic output feedback control laws are then designed for the SNNMs with inputs and outputs to stabilize the closed-loop systems. The control design equations are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms to determine the control signals. It is shown that most neural-network-based nonlinear systems can be transformed into input-output SNNMs to be stabilization synthesized in a unified way. Finally, some application examples are presented to illustrate the control design procedures.

A NEURAL NETWORK MODEL FOR CREDIT RISK EVALUATION

2009 ◽  
Vol 19 (04) ◽  
pp. 285-294 ◽  
Author(s):  
ADNAN KHASHMAN
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Credit Risk ◽  
Network Model ◽  
Neural Network Model ◽  
Risk Evaluation ◽  
Evaluation System ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Learning Schemes

Credit scoring is one of the key analytical techniques in credit risk evaluation which has been an active research area in financial risk management. This paper presents a credit risk evaluation system that uses a neural network model based on the back propagation learning algorithm. We train and implement the neural network to decide whether to approve or reject a credit application, using seven learning schemes and real world credit applications from the Australian credit approval datasets. A comparison of the system performance under the different learning schemes is provided, furthermore, we compare the performance of two neural networks; with one and two hidden layers following the ideal learning scheme. Experimental results suggest that neural networks can be effectively used in automatic processing of credit applications.

Establish an Artificial Neural Networks Model to Make Quantitative Analysis about the Capillary Electrophoresis Spectrum

2012 ◽  
Vol 452-453 ◽  
pp. 1116-1120
Author(s):  
Hong Ping Li ◽  
Hong Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Capillary Electrophoresis ◽  
Artificial Neural Networks ◽  
Quantitative Analysis ◽  
Network Model ◽  
Neural Network Model ◽  
Network Training ◽  
Dissociation Degree ◽  
Artificial Neural

Simulating the overlapping capillary electrophoresis spectrogram under the dissimilar conditions by the computer system , Choosing the overlapping capillary electrophoresis spectrogram simulated under the different conditions , processing the data to compose a neural network training regulations, Applying the artificial neural networks method to make a quantitative analysis about the multi-component in the overlapping capillary electrophoresis spectrogram,Using: Radial direction primary function neural network model and multi-layered perceptron neural network model. The findings indicated that, along with the increasing of the capillary electrophoresis spectrogram noise level, the related components’ ability of the two kinds of the overlapping capillary electrophoresis spectrogram by neural network model quantitative analysis drop down. Along with the increasing of the capillary electrophoresis spectrogram’s total dissociation degree, the multi-layered perceptron neural network model to the related components’ ability of the overlapping capillary electrophoresis spectum by quantitative analysis raise up.

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