European Control Conference (1993) Non-linear Recursive Identification and Control by Neural Networks: a General Framework

The development of engineering applications of neural networks makes it necessary to clarify the similarities and differences between the concepts and methods developed for neural networks and those used in more classical fields such as filtering and control. In previous papers [Nerrand et al. 1993], [Marcos et al. 1993], the relationships between non-linear adaptive filters and neural networks have been investigated, and a general framework has been introduced, which encompasses the recursive training of neural networks and the adaptation of non-linear filters. Out of this approach, three new families of training algorithms for feedback networks emerged; algorithms used routinely in adaptive filtering and in the training of neural networks were shown to be specific cases of this general approach. The adaptive identification of non-linear processes is a natural field of application of these algorithms. The first part of the paper will be devoted to a short survey of the recursive training of feedback (also termed recurrent) discrete-time neural networks for non-linear identification; the algorithms presented in that section can be used either for adaptive or for non-adaptive systems. Pursuing our effort along the same lines,we show that algorithms for the adaptive control of non-linear processes by neural networks can be derived from the above approach. However, process control has its own goals and constraints: therefore, the algorithms must be tuned to such specificities. The second part of the paper is devoted to the presentation of these algorithms, which are illustrated in detail in the third part.