Parsimonious Neurofuzzy Modelling

Modelling has become an invaluable tool in many areas of research, particularly in the control community where it is termed system identification. System identification is the process of identifying a model of an unknown process, for the purpose of predicting and/or gaining an insight into the behaviour of the process. Due to the inherent complexity of many real processes (i.e multivariate, nonlinear and time varying), conventional modelling techniques have proved to be too restrictive. In these instances more sophisticated (intelligent) modelling techniques are required. Recently the similarities between neural networks, with their ability to learn to universally approximate any continuous nonlinear multivariate function, and fuzzy systems, with their transparent reasoning by a series of linguistic rules, have been drawn. This has lead to the development of neurofuzzy systems combining the desired attributes of both these paradigms, and hence producing a technique ideal for modelling.