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| A weighted global iteration Parametric Kalman Filter Algorithm |
| Revised:March 12, 2001 |
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| DOI：10.7511/jslx20024086 |
| KeyWord:system identification,Parametric Kalman Filter,weighted global iteration,nonlinear system |
| Zhao Xin Li Jie |
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| Download times: 8 |
| Abstract: |
| By combining Parametric Kalman Filter Algorithm with weighted global iteration procedure, a weighted global iteration parametric Kalman Filter Algorithm(PKF\|WGI)is proposed . PKF algorithm can avoid the nonlinear coupling phenomenon between system parameters and state variables, and WGI procedure with an objective function is applied to obtain the stable and convergent solutions. The identification problems are investigated for single degree of freedom linear system and bilinear hysteretic systems. Ac cording to the numerical results, PKF\|WGI of weight 1 (i.e.: WGI without weight) is effective for the identification of linear system. While, An appropriate weight should be chosen to obtain good results for the identification of nonlinear system. When noise level is high, WGI with an objective function can ensure stable and convergent results. |
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