The theory analysis and simulation results show that the estimation is asymptotically unbiased and has strong consistency, and that the new method is very efficient and practical.

理论分析和仿真结果都表明估计结果具有渐近无偏性和一致收敛性，该方法辨识精度高，具有良好的实用性。

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In this paper, empirical Euclidean likelihood ratio statistics are constructed for parametric in a nonlinear model. And prove strong consistency and asymptotic normality of the estimation.

本文构造了非线性模型中参数的经验欧氏似然比统计量，并证明了该似然估计的强相合性和渐近正态性。

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And the strong consistency of the parameters estimation in this model is proved under some weaker conditions. It is also given that almost sure convergence rate of these estimates.

在较弱条件下证明了所获得的估计的强相合性，并给出了收敛速度。

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