TABLE OF CONTENTS This chapter has introduced the basic concepts related with the use of neural networks for nonlinear systems identification, and has briefly reviewed neuro-control approaches. Several important issues for the design of data-driven models, as neural networks are, such as data acquisition and the design of excitation signals could not be covered here, but they will be discussed in Chapter 5. On purpose, important topics such as neuro-fuzzy and local linear models were not discussed, as they will be treated in other chapters of this book.
Neural network modelling is an iterative process, requiring, at the present stage, substantial skills and knowledge from the designer. It is our view that the methodology presented in the example, employing multi-objective evolutionary algorithms for the design of neural models, is a suitable tool to aid the designer in this task. It incorporates input model order and structure selection, as well as parameter estimation, providing the designer with a good number of well performing models with varying degrees of complexity. It also allows the incorporation of objectives which are specific for the ultimate use of the model. Through the analysis of the results obtained in one iteration, the search space can be reduced for future iterations, therefore allowing a more refined search in promising model regions.
