Echo State Network for Abrupt Change Detections in Non-stationary Signals

The issue of abrupt change detection (ACD) in non-stationary time series signal is considered as a signal classification problem in this paper. A novel reservoir-computing based neural network model (RCNN) is proposed. The main component of RCNN is a large size, sparsely and randomly interconnected dynamical reservoir (DR), which is followed by a single layer perceptron (SLP). The signal containing abrupt changes is firstly projected into the high dimensional state space of DR, and then is linearly classified by the SLP. The SLP is trained by the delta leaning rule. The classification brought out by the SLP is the ACD result. Two synthetic non-stationary time series signals, one is non-chaotic, another one is chaotic, are verified on the RCNN respectively. The simulation experiment results show that the ACD performance of the proposed RCNN is comparable with that of the segment function embedded in MATLAB for the non-chaotic signal, and even outperforms for another chaotic signal. It is concluded that RCNN is a more efficient ACD technique.