Feed-forward neural nets

An ANN is a set of interconnected neurons (also termed nodes, cells, units or process elements distributed in a specific arrangement, usually termed an architecture. In general, neurons are organised in layers (see Figure 5.3).The most common neural nets, the feed-forward nets, are fully connected, i.e. each node is connected to all the nodes in the next layer. The information we want to enter in... 

Some of the pioneering studies published by several reputed authors in the chemometrics field [55] employed Kohonen neural networks to diagnose calibration problems related to the use of AAS spectral lines. As they focused on classifying potential calibration lines, they used Kohonen neural networks to perform a sort of pattern recognition. Often Kohonen nets (which were outlined briefly in Section 5.4.1) are best suited to perform classification tasks, whereas error back-propagation feed-forwards (BPNs) are preferred for calibration purposes [56]. 

K. Hosnik, M. Stinchcombe, and M. White, Universal approximation of an unknown mapping and its derivatives using multilayer feed-forward networks. Neural Net., 3 551-560 (1990). 

Modeling and forecasting. Modeling involves training the neural network on input-output data, such that an existing relationship between input and output data are represented sufficiently accurate. The relationship can be either static, in which case usually a feed-forward network is used. It could also be that the relationship is dynamic, in this case usually a recurrent neural net is used. Numerous applications can be found in the literature in different application areas, such as waste water plant modeling, data reconciliation, and so forth, see for example Miller et al. (1997), Meert (1998), Zhao et al. (1999), Basak et al. (2000) and Veltri et al. (2002). 

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