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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xuewen Wu Limin Zha |
| Copyright Year | 2008 |
| Description | Author affiliation: Coll. of Comput. & Inf. Eng., Hohai Univ., Nanjing (Xuewen Wu; Limin Zha) |
| Abstract | An identification method based on hybrid neural networks for Wiener model is investigated in this paper to analyze the nonlinear dynamic system of intelligence sensor, and the corresponding algorithm is presented. In this model, the nonlinear dynamic characteristic of sensor is expressed by cascading a linear dynamic subunit (LDS) with a nonlinear static subunit (NLSS). According to the characteristic of the model, an LDN linear neural network (LDN-LNN) simulating the LDS and a PID nonlinear neural network (PID-NLNN) simulating the NLSS form a hybrid neural network (HNN), which is used to identify Wiener model. By means of the HNN approach, the parameter of the model can be identified and separated into two parts simultaneously, one part is the coefficient of the LDS, the other is the coefficient of the NLSS. The simulation has proved the efficiency of the proposed method. |
| Starting Page | 1801 |
| Ending Page | 1806 |
| File Size | 173590 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424421831 |
| DOI | 10.1109/ICINFA.2008.4608299 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-06-20 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Heuristic algorithms Neurons Artificial neural networks Mathematical model Artificial intelligence MATLAB Equations |
| Content Type | Text |
| Resource Type | Article |
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