I. Introduction

In the preceding chapters we focused on parameter estimation methods suitable for phenomenological models based on the physical insight. Developing such models, which we usually prefer because it leads to better understanding of the underlying physics, can be highly demanding. This is particularly the case when the phenomenon being investigated is highly nonlinear, for example, in our case, aerodynamic effects at separated flow conditions. Since we wish to estimate and validate such models from measured input output data, the artificial neural networks (ANN) provide an alternative approach to model building.1 7 The ANNs are neuroscience-inspired computational tools, extensively used for pattern recognition. They provide a general framework for nonlinear functional mapping of the input output subspace, and as such are part of the system identification methodology which deals implicitly with the measured input output data. The ANNs, or simply termed NNs, are alternatively called computational neural networks to clearly differentiate them from the biological neural networks. They are also referred to as universal approximators or connectionist models.

Historically, the concept of neural networks can be traced to McCulloch and Pitts, a neuroanatomist and a mathematician, who showed in 1943 that principally any arithmetic or logical function can be computed using a network consisting of simple neurons.8 The neurons were binary switches, which were triggered in the absence of any inhibitory input when the sum of excitatory inputs exceeded a threshold. This led to realization of the Boolean functions such as AND, OR, or XOR, the basic building blocks of a...