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全局优化方法在MEG源定位问题中的应用和比较研究
Alternative TitleThe Application and Comparative Study of Global Optimization Approaches to MEG Source Localization
李晓东
Subtype工学硕士
Thesis Advisor蒋田仔
2000-06-01
Degree Grantor中国科学院自动化研究所
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Abstract在过去的两年中,我们主要从事用全局优化方法解决MEG源定位问题和医学图 象处理方面的研究,具体包括遗传算法,模拟退火算法,禁忌搜索算法在MEG源定 位问题上的应用和性能比较和这些方法的性能比较。由于篇幅原因,本文中我们略 去了医学图象重建方面的工作.本论文组织如下: 第一章介绍了我的工作的背景和起因,介绍了研究优化方法在MEG和医学图象 处理领域的意义和必要性,说明了我们为什么选择使用全局优化方法来求解MEG源 定位问题.然后介绍了我们的工作的主要贡献和论文的组织. 第二章简单介绍了MEG的基本概念,MEG正问题和MEG的逆问题的数学模型. 第三章具体介绍了优化问题中的一些概念以及在我们的工作中遗传算法,模拟退 火算法,禁忌搜索算法在MEG源定位问题上的具体实现细节. 第四章中,我们设计了计算机模拟实验,将遗传算法,模拟退火算法,禁忌搜索算法应用到了这个问题上.分析了每一种算法在这个问题上的性能.然后给出了几种算法的性能比较.我们的实验表明,这几种优化算法在MEG源定位问题上有很好的效果.其中,我们设计的遗传混合算法效果最佳,模拟退火算法性能也很出色,但它的计算量要稍差大于遗传混合算法,禁忌搜索算法性能不如我们预期的好.它在一些情况下,性能很好,但在一些很复杂的情况下,性能不如前两种算法.
Other AbstractIn the past two years, we have been working on MEG source localization and medical image processing using global optimization methods. Our major work includes the application of Genetic Algorithm, Simulated Annealing Algorithm, Taboo Search Algorithm to MEG source localization problem and the comparison of the performance of these methods. In order not to make it too long, this thesis does not include our work on medical image reconstruction. The thesis is organized as follows. In Chapter 1 we describe the necessity of our research into the optimization approaches to the MEG source analysis and medical image analysis. We also give a brief description of our major contributions and the organization of my thesis. Chapter 2 devotes to a description of the basic theory of MEG and the formulation of the MEG forward problem and inverse problem. Chapter 3 deals with the genetic algorithm, simulated annealing algorithm and taboo search algorithm. We give the details of our implementation of these algorithms on MEG source localization problem. In Chapter 4, We describe our design of the computer simulations of the algorithms described in the above chapter. We apply our genetic hybrid algorithm, simulated annealing algorithm and taboo search algorithm to this problem. Then we compare their performance on MEG source localization problem. Our results show that these algorithms all have the potential to find the dipole parameters correctly. As to the performance, our genetic hybrid algorithm performs best. Simulated annealing algorithm performs also well, but its computational cost is a little bit larger than genetic hybrid algorithm. Taboo search algorithm is not as good as expected. In some case, it performs very well, but in some difficult cases, it performs not as good as the genetic algorithm and simulated annealing do.
shelfnumXWLW560
Other Identifier560
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/7293
Collection毕业生_硕士学位论文
Recommended Citation
GB/T 7714
李晓东. 全局优化方法在MEG源定位问题中的应用和比较研究[D]. 中国科学院自动化研究所. 中国科学院自动化研究所,2000.
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