复杂系统的随机学习与分析算法及其在DNA分析中的应用

自组织学习，演化算法，神经网络，模拟退火隐MARKOV模型，以及模拟MARKOV样本法等在对付大规复杂系统中出现的组合爆炸及非线性是很有力的工具，它们具有通用、稳健、简单，等重要优点。本研究可以说是对于象DNA序列分析这样未知结构，但在21世纪理论与应用两矫婢哂兄匾庖宓奈侍獾墓丶际酢

Following the development of large and high performance computers, it becomes possible to study and to process the large complex systems by computers and this task is putting on schedule. Many new algorithms are posed to meet the requirement of the research. This project is devoted to the research on the mathematical models, analysis methods and algorithms for the complex systems , such as the DNA sequences, which are the blue prints of human beings and other organisms. The theoretical analysis and applications on the algorithms, such as HMM, linguistic models, self-organized learning, etc., are considered. The stochastic resonance is studied and its relation to molecular motors, which widely attracting tremendous amount of attention, also considered.