Processing short-term and long-term information with a combination of polynomial approximation techniques and time-delay neural networks.
Neural networks are often used to process temporal information, i.e., any kind of information related to time series. In many cases, time series contain short-term and long-term trends or behavior. This paper presents a new approach to capture temporal information with various reference periods simultaneously. A least squares approximation of the time series with orthogonal polynomials will be used to describe short-term trends contained in a signal (average, increase, curvature, etc.). Long-term behavior will be modeled with the tapped delay lines of a time-delay neural network (TDNN). 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 artificial data and two real-world application examples, the prediction of the user number in a computer network and online tool wear classification in turning.
Fuchs
Erich
E
Faculty of Informatics and Mathematics, University of Passau, Passau 94032, Germany. fuchse@fim.uni-passau.de
Gruber
Christian
C
Reitmaier
Tobias
T
Sick
Bernhard
B
D000465:Algorithms; D003198:Computer Simulation; D003201:Computers; D016208:Databases, Factual; D004864:Equipment and Supplies; D005544:Forecasting / Q000379:methods; D006801:Humans; D016018:Least-Squares Analysis; D016014:Linear Models; D015233:Models, Statistical; D016571:Neural Networks, Computer; D009474:Neurons; D017711:Nonlinear Dynamics; D010507:Periodicity; D013997:Time Factors