Use of Neural Net Computing Statistical Modelling

At the beginning of this chapter, we introduced statistical models based on the general principle of the Taylor function decomposition, which can be recognized as non-parametric kinetic model. Indeed, this approximation is acceptable because the parameters of the statistical models do not generally have a direct contact with the reality of a physical process. Consequently, statistical models must be included in the general class of connectionist models (models which directly connect the dependent and independent process variables based only on their numerical values). In this section we will discuss the necessary methodologies to obtain the same type of model but using artificial neural networks (ANN). This type of connectionist model has been inspired by the structure and function of animals natural neural networks. 

Neural nets are computing programs that behave externally as multi-input multi-output computing blocks. Although artificial neural networks were initially devised for parallel processing, they are being used on sequential machines (von Neumann) as well. 

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