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• W2718943689 abstract "Abstract Preprocessing is recognized as an important tool in modelling, particularly when the data or underlying physical process involves complex nonlinear dynamical interactions. In this paper, we will give a review of preprocessing methods used in linear and nonlinear models. We will consider first the problem of static preprocessing, where no dependence on time between the input vector elements is assumed. We then consider dynamic preprocessing methods which involve the modification of time dependent input values before they are used in the linear or nonlinear models. Furthermore the problem of 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 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." @default.
• W2718943689 created "2017-06-30" @default.
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• W2718943689 date "1994-12-01" @default.
• W2718943689 modified "2023-09-25" @default.
• W2718943689 title "Static and Dynamic Preprocessing Methods in Neural Networks" @default.
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• W2718943689 doi "https://doi.org/10.1016/s1474-6670(17)45725-9" @default.
• W2718943689 hasPublicationYear "1994" @default.
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