Abstract
A new parallel method for on-line parameter identification by neural networks is proposed to realize the real-time identification. The Hopfield networks is modified by replacing its sigmoidal function with multi-linear function, and then the link matrix and bias of the modified networks are set to guarantee the stability of the networks and to guarantee that the only one equilibrium of the networks be the least squares solution of the parameter identification. In order to meet the requirement of on-line identification, the recurrence formula is given. The simulation results of an underwater vehicle system with the new method show the validity of the method through comparison with the results obtained with traditional least squares identification method.
| Original language | English |
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| Pages (from-to) | 244-248 |
| Number of pages | 5 |
| Journal | Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University |
| Volume | 15 |
| Issue number | 2 |
| State | Published - 1997 |