STATIC AND DYNAMIC PREPROCESSING METHODS IN NEURAL NETWORKS

Preprocessing is recognized as an important tool in modeling, particularly when the data or underlying physical process involves complex nonlinear dynamical interactions. This paper will give a review of preprocessing methods used in linear and nonlinear models. The problem of static preprocessing will be considered first, where no dependence on time between the input vectors is assumed. Then, dynamic preprocessing methods which involve the modification of time-dependent input values before they are used in the linear or nonlinear models will be considered. Furthermore, the problem of an insufficient number of input vectors is considered. It is shown that one way in which this problem can be overcome is by expanding the weight vector in terms of the available input vectors. Finally, a new problem which involves both cases of: (1) transformation of input vectors; and (2) insufficient number of input vectors is considered. It is shown how a combination of the techniques used to solve the individual problems can be combined to solve this composite problem. Some open issues in this type of preprocessing methods are discussed.