Neural multi-layer-feed-forward network

Derks et al. [70] employed ANNs to cancel out noise in ICP. The results of neural networks (an Adaline network and a multi-layer feed-forward network) were compared with the more conventional Kalman filter. 

J. R. M. Smits, W. J. Meissen, L. M. C. Buydens and G. Kateman, Using artificial neural networks for solving chemical problems. Part I multi-layer feed-forward networks, Chemom. Intell. Lab. Syst., 22(2), 1994, 165-189. 

D. Svozil, Introduction to multi-layer feed-forward neural networks. Chemom. Intell. Lab. Syst., 39 (1997) 43-62. 

W.J. Meissen and L.M.C. Buydens, Aspects of multi-layer feed-forward neural networks influencing the quality of the fit of univariate non-linear relationships. Anal. Proc., 32 (1995) 53-56. 

Zhang et al. [78] analysed the metal contents of serum samples by ICP-AES (Fe, Ca, Mg, Cr, Cu, P, Zn and Sr) to diagnose cancer. BAM was compared with multi-layer feed-forward neural networks (error back-propagation). The BAM method was validated with independent prediction samples using the cross-validation method. The best results were obtained using BAM networks. 

Figure 12.1. Multi-layered Feed Forward Neural Network Architecture...

Neural network architectures 2L/FF = two-layer, feed forward network (i.e., perceptron) 3L or 4L/FF = three- or four-layer, feed-forward network (i.e., multi-layer perceptron). 

As a last resort it is possible to apply neural networks (NN). NN can in principle model surfaces with any complexity. However, the number of experiments required is laige. This, together with the fact that NN is a rather specialised technique, explains that the number of applications in the literature is limited. Examples are to be found in 70-72). In the latter application two variables (pH and modifier content) are investigated for four chlorophenols and the authors found that when 15 to 20 experiments are carried out, better results are obtained with a multi-layer feed-forward NN than when using quadratic or third-order models. Although we believe that for the optimization of separations, NN will prove practical only in few cases, it seems useful to explain the first principles of the methodology here. A simple network is shown in Fig. 6.25. 

Using these experimental data, artificial neural network models for predicting fractal dimension have been developed using multi-layer feed-forward back propagation algorithm. 

Fig. 27.1. Example of a multi-layer feed-forward neural network.

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