Application of four-layer neural network on information extraction

Han, Min; Cheng, Lei; Meng, Hua

doi:10.1016/s0893-6080(03)00120-5

Cited by 20 publications

(8 citation statements)

References 3 publications

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“…For wetlands, improvements in classification accuracy relative to the maximum likelihood method have been reported with, e.g., classification trees (Davranche et al, 2010;Na et al, 2009), support vector machines (SVM; Fung et al, 2009;Sanchez-Hernandez et al, 2007;Wang et al, 2012), and artificial neural networks (Berberoglu et al, 2004;Fung et al, 2009;Han et al, 2003;Zeng et al, 2007). However, little is still known on relative performance of various supervised approaches, and very few comprehensive method comparisons have been reported so far (Clinton et al, 2010b;Richmond, 2011).…”

Section: Introductionmentioning

confidence: 99%

Landscape analysis of wetland plant functional types: The effects of image segmentation scale, vegetation classes and classification methods

Dronova

Gong

Clinton

et al. 2012

Remote Sensing of Environment

143

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Section: Introductionmentioning

confidence: 99%

Landscape analysis of wetland plant functional types: The effects of image segmentation scale, vegetation classes and classification methods

Dronova

Gong

Clinton

et al. 2012

Remote Sensing of Environment

143

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“…Typically, an ANN has just one or two hidden layers (Lin et al 2008). The two-hidden-layer neural network can be adopted to solve comparatively complex problems rather than the singlehidden-layer structure (Han et al 2003). The four-layer (one input layer, one output layer, and two hidden layers) ANN is adopted to ensure high training accuracy.…”

Section: Annmentioning

confidence: 99%

Inverse design of aircraft cabin environment by coupling artificial neural network and genetic algorithm

Zhang

You

2014

HVAC&R Research

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An inverse design method is proposed to achieve the pre-set control objectives of the aircraft cabin environment. The method combines the artificial neural network and the genetic algorithm, and the training and testing data of the artificial neural network is obtained by computational fluid dynamics analysis. Both of the thermal comfort and energy consumption are considered in the inverse design. The artificial neural network is used to identify the relationship between the thermal comfort and the air supply parameters (inlet velocity magnitude and angle, inlet air temperature). The genetic algorithm coupled with the well-trained artificial neural network is used to design the aircraft cabin environment. Numerical results show that the Bayesian regularization algorithm is proved to have better generalization capability than the other training algorithms for the artificial neural network. The increase of training data quantity improves the generalization capability of the artificial neural network, while it spends more simulation time. A computational fluid dynamics database with 60 datasets is shown to be suitable to the present inverse design, and the testing error of the artificial neural network is below 8.2%. Several groups of optimal air supply parameters are found with different trade-offs between the thermal comfort and energy consumption. The best solution of thermal comfort, i.e., the percentage of zone with |PMV | >0.5 in all cabin control domains, is less than 7.8%.

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“…Usually, a neural network with a single hidden layer can solve most prediction and classification problems. Nevertheless, if samples are comparatively complex, multilayer neural networks can be adopted to realize the complex nonlinear mapping .…”

Section: Model Developmentmentioning

confidence: 99%

Blast Furnace System Modeling by Multivariate Phase Space Reconstruction and Neural Networks

Gao

Zeng

et al. 2012

Asian Journal of Control

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Time series data collected from a medium‐size blast furnace (BF) is analyzed using the phase space reconstruction. To achieve better reconstruction, multivariate correlation analysis is first applied to screen out correlated variables, which shows that three important variables, i.e., silicon content in hot metal ([Si]), permeability index (FF), and coal injection (PM), are most appropriate for multivariate reconstruction. The time delay and embedding dimension are determined via the autocorrelation function and false nearest neighbor method. With the reconstructed time series, the neural networks model is applied to construct the predictive model for silicon content in hot metal. The simulation shows that the models based on multivariate reconstruction give better predictions than those obtained by univariate reconstruction. Moreover, it reveals that multivariate reconstruction can greatly mitigate the drawbacks caused by insufficiency of data.

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Application of four-layer neural network on information extraction

Cited by 20 publications

References 3 publications

Landscape analysis of wetland plant functional types: The effects of image segmentation scale, vegetation classes and classification methods

Landscape analysis of wetland plant functional types: The effects of image segmentation scale, vegetation classes and classification methods

Inverse design of aircraft cabin environment by coupling artificial neural network and genetic algorithm

Blast Furnace System Modeling by Multivariate Phase Space Reconstruction and Neural Networks

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