Memorising the data

VR, the inputs correspond to the value of the various parameters and the network is 1 to reproduce the experimentally determined activities. Once trained, the activity of mown compound can be predicted by presenting the network with the relevant eter values. Some encouraging results have been reported using neural networks, have also been applied to a wide range of problems such as predicting the secondary ire of proteins and interpreting NMR spectra. One of their main advantages is an to incorporate non-linearity into the model. However, they do present some problems Hack et al. 1994] for example, if there are too few data values then the network may memorise the data and have no predictive capability. Moreover, it is difficult to the importance of the individual terms, and the networks can require a considerable 1 train. 

Artificial neural networks (ANNs) emulate some human brain characteristics, such as the ability to derive conclusions from fuzzy input data, the capacity to memorise patterns and a high potential to relate facts (samples). If we examine carefully those human (animal) abilities, they share a common basis they cannot be expressed through a classical well-defined algorithm rather, they are based on a common characteristic experience. Humans can solve situations according to their accumulated experience, rather on a conscious and strict reasoning procedure. 

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