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For ANN model

In the multivariate calibration field, Khayatzadeh et al. [65] compared ANNs with PLS to determine U, Ta, Mn, Zr and W by ICP in the presence of spectral interferences. For ANN modelling, a PCA preprocessing was found to be very effective and, therefore, the scores of the first five dominant PCs were input to an ANN. The network had a linear transfer function on both the hidden and output layers. They used only 20 samples for training. [Pg.272]

Figure 1. Land slide s triggered by the Yushu earthquake, training and testing data for ANN modeling. Figure 1. Land slide s triggered by the Yushu earthquake, training and testing data for ANN modeling.
We calculate for ANNs model mean absolute error (MAE) and mean square error (MSE). The MSE of ANNs model is compared with MSE of UNIQUIC which is reported by Iglesias et al. (2007). The result shows the ANNs model have a better agreement with the experimental data at higher temperature in see Table 17.3. [Pg.170]

The data used for ANN model training and learning is organised as shown in Table 10.5, which lists data that are within the following guidelines (Sii (2001), Wang et al. (2001)) ... [Pg.248]

The ANN model had four neurones in the input layer one for each operating variable and one for the bias. The output was selected to be cumulative mass distribution thirteen neurones were used to represent it. A sigmoid functional... [Pg.274]

Figure 25 ANN model (5-8-6) training and testing results for van der Waals volume, molar volume, heat capacity, solubility parameter, and glass transition temperature of 45 different polymers. Figure 25 ANN model (5-8-6) training and testing results for van der Waals volume, molar volume, heat capacity, solubility parameter, and glass transition temperature of 45 different polymers.
Figure 30 ANN model training and testing results for crosslink density changes due to aging. Figure 30 ANN model training and testing results for crosslink density changes due to aging.
Bourquin J, Schmidli H, van Hoogevest P, Leuenberger H. Pitfalls of artificial neural networks (ANN) modelling technique for data sets containing outlier measurements using a study of mixture properties of a direct compressed tablet dosage form. Eur J Pharm Sci 1998 7 17-28. [Pg.699]

For PyMS to be used for (1) routine identification of microorganisms and (2) in combination with ANNs for quantitative microbiological applications, new spectra must be comparable with those previously collected and held in a data base.127 Recent work within our laboratory has demonstrated that this problem may be overcome by the use of ANNs to correct for instrumental drift. By calibrating with standards common to both data sets, ANN models created using previously collected data gave accurate estimates of determi-nand concentrations, or bacterial identities, from newly acquired spectra.127 In this approach calibration samples were included in each of the two runs, and ANNs were set up in which the inputs were the 150 new calibration masses while the outputs were the 150 old calibration masses. These associative nets could then by used to transform data acquired on that one day to data acquired at an earlier data. For the first time PyMS was used to acquire spectra that were comparable with those previously collected and held in a database. In a further study this neural network transformation procedure was extended to allow comparison between spectra, previously collected on one machine, with spectra later collected on a different machine 129 thus calibration transfer by ANNs was affected. Wilkes and colleagues130 have also used this strategy to compensate for differences in culture conditions to construct robust microbial mass spectral databases. [Pg.333]

Both cases can be dealt with both by supervised and unsupervised variants of networks. The architecture and the training of supervised networks for spectra interpretation is similar to that used for calibration. The input vector consists in a set of spectral features yt(Zj) (e.g., intensities at selected wavelengths zi). The output vector contains information on the presence and absence of certain structure elements and groups fixed by learning rules (Fig. 8.24). Various types of ANN models may be used for spectra interpretation, viz mainly such as Adaptive Bidirectional Associative Memory (BAM) and Backpropagation Networks (BPN). The correlation... [Pg.273]

Development of ANN model for estimating VLE is less cumbersome than methods based on EOS. It does not require parameters such as the critical properties of the components or the... [Pg.256]

Table 12.7 The model fit (RMSEE) values of 7 different neural net (ANN) models for predicting cis-butadiene content in styrene-butadiene copolymers by NIR spectroscopy using 1 to 6 nodes in the hidden layer... Table 12.7 The model fit (RMSEE) values of 7 different neural net (ANN) models for predicting cis-butadiene content in styrene-butadiene copolymers by NIR spectroscopy using 1 to 6 nodes in the hidden layer...
Unfortunately, the ANN method is probably the most susceptible to overfitting of the methods discussed thus far. For similar N and M, ANNs reqnire many more parameters to be estimated in order to define the model. In addition, cross validation can be very time-consuming, as models with varying complexity (nnmber of hidden nodes) mnst be trained individually before testing. Also, the execntion of an ANN model is considerably more elaborate than a simple dot product, as it is for MLR, CLS, PCR and PLS (Eqnations 12.34, 12.37, 12.43 and 12.46). Finally, there is very little, or no, interpretive value in the parameters of an ANN model, which eliminates one nseful means for improving the confidence of a predictive model. [Pg.388]

For the styrene-butadiene copolymer application, fit results of ANN models using one to six hidden nodes are shown in Table 12.7. Based on these results, it appears that only three, or perhaps four, hidden nodes are required in the model, and the addition of more hidden nodes does not greatly improve the fit of the... [Pg.388]

Like ANNs, SVMs can be useful in cases where the x-y relationships are highly nonlinear and poorly nnderstood. There are several optimization parameters that need to be optimized, including the severity of the cost penalty , the threshold fit error, and the nature of the nonlinear kernel. However, if one takes care to optimize these parameters by cross-validation (Section 12.4.3) or similar methods, the susceptibility to overfitting is not as great as for ANNs. Furthermore, the deployment of SVMs is relatively simpler than for other nonlinear modeling alternatives (such as local regression, ANNs, nonlinear variants of PLS) because the model can be expressed completely in terms of a relatively low number of support vectors. More details regarding SVMs can be obtained from several references [70-74]. [Pg.389]

M. Blanco and A. Peguero, An expeditious method for determining particle size distribution by near infrared spectroscopy comparison of PLS2 and ANN models, Talanta, 77(2), 647-651 (2008). [Pg.490]

Electronic descriptors were calculated for the ab initio optimized (RHG/STO-3G) structures. In addition, logP as a measure of hydrophobicity and different topological indices were also calculated as additional descriptors. A nonlinear model was constructed using ANN with back propagation. Genetic algorithm (GA) was used as a feature selection method. The best ANN model was utilized to predict the log BB of 23 external molecules. The RMSE of the test set was only... [Pg.110]


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