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本文对梯度正则化方法(GradientRegularizationMethod)作了进一步的研究,给出一种建立了梯度正则化迭代算法和选择正则参数的简明实用方法。文中椭圆算子方程参数识别算例不仅说明了GR法具有广泛的适用性和一定的抗噪音能力,而且收敛速度较快,具有较大的收敛范围。
In this paper, the GradientRegularizationMethod is further studied, and a concise and practical method of establishing a gradient regularization iteration algorithm and selecting regular parameters is given. The example of elliptic operator equation parameter identification not only shows that GR method has a wide range of applicability and a certain anti-noise ability, but also has a fast convergence rate and a large convergence range.