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Recurrent Neural Networks Architectures Perspective In this chapter, the use of neural networks, in particular recurrent neural networks, in system identification, signal processing and forecasting is considered. The ability of neural networks to model nonlinear dynamical systems is demonstrated, and the correspondence between neural networks and block-stochastic models is established. Finally, further discussion of recurrent neural network architectures is provided. | Recurrent Neural Networks for Prediction Authored by Danilo P. Mandic Jonathon A. Chambers Copyright 2001 John Wiley Sons Ltd ISBNs 0-471-49517-4 Hardback 0-470-84535-X Electronic 5 Recurrent Neural Networks Architectures Perspective In this chapter the use of neural networks in particular recurrent neural networks in system identification signal processing and forecasting is considered. The ability of neural networks to model nonlinear dynamical systems is demonstrated and the correspondence between neural networks and block-stochastic models is established. Finally further discussion of recurrent neural network architectures is provided. Introduction There are numerous situations in which the use of linear filters and models is limited. For instance when trying to identify a saturation type nonlinearity linear models will inevitably fail. This is also the case when separating signals with overlapping spectral components. Most real-world signals are generated to a certain extent by a nonlinear mechanism and therefore in many applications the choice of a nonlinear model may be necessary to achieve an acceptable performance from an adaptive predictor. Communications channels for instance often need nonlinear equalisers to achieve acceptable performance. The choice of model has crucial importance1 and practical applications have shown that nonlinear models can offer a better prediction performance than their linear counterparts. They also reveal rich dynamical behaviour such as limit cycles bifurcations and fixed points that cannot be captured by linear models Gershenfeld and Weigend 1993 . By system we consider the actual underlying physics2 that generate the data whereas by model we consider a mathematical description of the system. Many variations of mathematical models can be postulated on the basis of datasets collected from observations of a system and their suitability assessed by various performance 1 System identification for instance consists of .