中文摘要：

输入一输出数据是解决系统辨识问题的关键要素，传统的辨识理论除了假定影响输入．输出数据干扰的密度函数已知外，还要假定输入一输出数据能够精确获得，完全忽略了所用数据的质量．本文突破了传统理论的两个假设，首先用工程上易于获得的干扰的有界集合代替干扰的密度函数，并在特定数据不确定性结构下，考虑了数据质量问题，然后，以半定规划为基础，导出了鲁棒对等式，从而将系统辨识转化为对数据质量具有鲁棒性的优化问题，通过求解该优化问题，得到了一种新的鲁棒优化辨识方法，仿真结果表明了新方法的可行性和有效性．

英文摘要：

Input-output data is a key element in solving the problem of system identification. The traditional identification theory takes into account the assumptions that the density func- tion of the disturbance is known and the input-output data can be accurately obtained, while completely ignores the quality of the data used. In the paper, to overcom the limitation of the two assumptions, a bounded set is firstly taken which can be ob- tained easily in engineering as an alternative to the density func- tion. Subsequently, with the specific uncertain data structure and considering the effect of the data quality, robust counterpart is derived by the semi-definite programming theory. And, the system identification problem is converted to an optimization problem which is robust to the uncertain data. By solving the optimization problem, a new identification algorithm based on robust optimization is proposed. Simulation results show the feasibility and effectiveness.