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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Qingsong Song Zuren Feng Liangjun Ke Min Li |
| Copyright Year | 2009 |
| Abstract | 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. |
| Starting Page | 226 |
| Ending Page | 231 |
| File Size | 288658 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424447350 |
| DOI | 10.1109/ISDA.2009.38 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-11-30 |
| Publisher Place | Italy |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Chaos Design engineering Neural networks Neurons Signal processing Reservoirs Systems engineering and theory State-space methods Mathematical model Signal detection |
| Content Type | Text |
| Resource Type | Article |
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