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Artificial neural network (ANN) models have been extensively studied with the aim of achieving human-like performance, especially in the field of pattern recognition. These networks are composed of a number of nonlinear computational elements which operate in parallel and are arranged in a manner reminiscent of biological neural interconnections. ANNs are known by many names such as connectionist models, parallel distributed processing models and neuromorphic systems (Lippmann 1987). | 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 1 Introduction Artificial neural network ANN models have been extensively studied with the aim of achieving human-like performance especially in the field of pattern recognition. These networks are composed of a number of nonlinear computational elements which operate in parallel and are arranged in a manner reminiscent of biological neural interconnections. ANNs are known by many names such as connectionist models parallel distributed processing models and neuromorphic systems Lippmann 1987 . The origin of connectionist ideas can be traced back to the Greek philosopher Aristotle and his ideas of mental associations. He proposed some of the basic concepts such as that memory is composed of simple elements connected to each other via a number of different mechanisms Medler 1998 . While early work in ANNs used anthropomorphic arguments to introduce the methods and models used today neural networks used in engineering are related to algorithms and computation and do not question how brains might work Hunt et al. 1992 . For instance recurrent neural networks have been attractive to physicists due to their isomorphism to spin glass systems Ermentrout 1998 . The following properties of neural networks make them important in signal processing Hunt et al. 1992 they are nonlinear systems they enable parallel distributed processing they can be implemented in VLSI technology they provide learning adaptation and data fusion of both qualitative symbolic data from artificial intelligence and quantitative from engineering data they realise multivariable systems. The area of neural networks is nowadays considered from two main perspectives. The first perspective is cognitive science which is an interdisciplinary study of the mind. The second perspective is connectionism which is a theory of information .