Abstract
M.Ing.
This dissertation discusses the results of a literature survey into the theoretical aspects and development of recurrent neural networks. In particular, the various architectures and training algorithms developed for recurrent networks are discussed. The various characteristics of importance for the efficient implementation of recurrent neural networks to model dynamical nonlinear processes have also been investigated and are discussed. Process control has been identified as a field of application where recurrent networks may play an important role. The model based adaptive control strategy is briefly
introduced and the application of recurrent networks to both the direct- and the indirect adaptive control strategy highlighted. In conclusion, the important areas of future research for the successful implementation of recurrent networks in adaptive nonlinear control are identified