Processing short-term and long-term information with a combination of hardand soft-computing techniques

Neural networks often must process temporal information, i.e. any kind of information related to a time series. In many of these cases time series contain short-term and long-term information (e.g. trends or periodic behavior). The article presents a new approach which combines hard- and soft-computing techniques to capture information with various reference time windows simultaneously. A least-squares approximation of time series with orthogonal polynomials will be used to infer information about short-term information contained in a signal (average, increase, curvature, etc.). Long-term information will be modeled using the Dynamic Neural Network (DYNN) paradigm,. This network takes the coefficients of the orthogonal expansion of the approximating polynomial as inputs such considering short-term and long-term information efficiently. The advantages of the method will be demonstrated by means of two real-world application examples, the prediction of the user number in a PC-pool and online tool wear classification in turning.