Constructive learning-industrial perspectives
Roderick Murray‐Smith, Kenneth James Hunt, Frieder Lohnert · 1994
The learning algorithms and structures which have become popular in the neural network community over the last few years have been successfully applied to various challenging modelling problems. In contrast to the linear modelling methods, there is still no clearly defined engineering process from which a model can reliably be created from measurements taken from a physical system. Problems for the practical application of neural networks involve the interpretation of the trained models, and the explicit introduction of a priori models into the learning system, as well as the use, in many cases, only basic ad hoc validation and experiment design techniques. This talk will discuss several methods which can improve the engineering aspects of model identification with neural nets.