Filtering and differentiating noisy signals using neural networks
Martin Ulrich Schmidt, Oliver Nelles · 1998
Measured signals are difficult to differentiate. Measurement noise, quantization and jitter occur and in general cannot be eliminated by simple lowpass filtering. This paper presents a new approach for off-line filtering and differentiating sampled time series using a special kind of neural networks, namely an extension of the local linear model tree (LOLIMOT). LOLIMOT is a tree construction algorithm based on the idea of approximating nonlinear functions by piecewise linear models. The filtered signal can be guaranteed not to leave a given band of tolerance, shows human-like smoothing effects and allows the introduction of a priori knowledge.