| 非线性反演算法的综合评价对比 |
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| 引用本文： | 王乐洋,许冉冉,靳锡波,丁锐.非线性反演算法的综合评价对比[J].武汉大学学报(信息科学版),2022,47(3):341-351. |
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| 作者姓名： | 王乐洋 许冉冉 靳锡波 丁锐 |
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| 作者单位： | 1.东华理工大学测绘工程学院，江西 南昌, 330013 |
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| 基金项目： | 国家自然科学基金41874001国家自然科学基金41664001国家自然科学基金42174011江西省自然科学基金20202BABL212015 |
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| 摘 要： | 结合蒙特卡罗方法的精度评定特点,提出了一种将偏差和中误差作为评价指标的综合评价公式.利用所提出的综合评价公式评价神经网络算法(neural network algorithm,NNA)、基因遗传算法(genetic algo-rithm,GA)和模拟退火算法(simulated annealing,SA)在火山复式位错...
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| 关 键 词： | 模拟退火算法 基因遗传算法 神经网络算法 非线性反演 综合评价对比 |
| 收稿时间： | 2020-11-11 |
Comprehensive Evaluation and Comparison of Nonlinear Inversion Algorithms |
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| Abstract: | Objectives As nonlinear models become more complex and data sources become more abundant, people have higher and higher requirements for nonlinear methods and precision.However, the existing research mainly considered the precision evaluation of the algorithm, and ignored how to comprehensively evaluate the applicability of the algorithm based on precision information obtained by calculation. Methods Considering the above problems, we propose a method to evaluate the advantages of algorithms based on precision information—non-linear comprehensive evaluation method. The method firstly evaluates the accuracy by the traditional Monte Carlo (MC) method, and uses the calculated deviation and the median error as the evaluation index, and calculates the comprehensive evaluation value according to the proposed comprehensive evaluation formula. We use the proposed method to evaluate the precision information of simulated annealing algorithm (SA), genetic algorithm (GA) and neural network algorithm (NNA) in volcano compound dislocation model (CDM) and seismic Okada model inversion. Results The experimental results show that whether it is based on the CDM model or the Okada model, the parameter estimation gap between the above three methods is small, but the precision of the different methods varies greatly. The MC is used to calculate the precision of SA, GA and NNA. It is found that in the volcano CDM, mean square errors in some parameters calculated by SA and GA are relatively large, and the comprehensive evaluation values 11.398 8 and 3.982 0 of SA and GA are larger than the comprehensive evaluation value 3.613 1 of NNA. In the Okada model, the SA has a higher mean square error than GA and NNA, and in the Lushan earthquake, the comprehensive evaluation value of the SA is 11.656 2, which is much larger than the comprehensive evaluation values 4.060 4 and 3.625 4 of the GA and NNA. Conclusions The precision information of the NNA is higher when the proposed method is used for evaluation, and the result is more convincing, followed by GA, and SA has lower precision. |
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