Nonconvex Stochastic Optimization for Model Reduction |
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Authors: | Han-Fu Chen Hai-Tao Fang |
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Affiliation: | (1) Laboratory of Systems and Control, Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100080, P.R. China |
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Abstract: | In this paper a global stochastic optimization algorithm, which is almost surely (a.s.) convergent, is applied to the model reduction problem. The proposed method is compared with the balanced truncation and Hankel norm approximation methods by examples in step responses and in approximation errors as well. Simulation shows that the proposed algorithm provides better results. |
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Keywords: | Stochastic optimization Model reduction Balanced truncation Hankel norm approximation |
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