论文总字数：38134字
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\title{基于稀疏贝叶斯学习的群体智能数据挖掘研究}
\author{刘有亮}
\date{\today}
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\fancyhead[l]{\kaishu{~~~东~南~大~学~本~科~毕~业~论~文}}
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\begin{center}{\kaishu\zihao{4} 摘\ \ \ \ 要}
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\addcontentsline{toc}{chapter}{摘\ \ \ \ 要} {\kaishu \ \
群集运动现象普遍存在于自然界，如鱼群、鸟群群、昆虫群乃至细菌菌落等。近年来群集运动以其独特的魅力吸引了生物学家、物理学家、计算机学家和系统控制学家的广泛研究。由于数据获取技术的限制，群体运动数据的采集十分困难，而早期基于假设的群体行为模型研究无法揭示自然界生命群体行为的本质机理，因此急需借助高精度设备获取的实证数据开展数据分析与数据挖掘的实证研究。随着当今大数据、人工智能时代的到来，人们可以更加方便快捷的获取大量的有价值的数据，因此以数据驱动的群集动力学的研究成为当下的热门研究方向。本文旨在探究群集运动的内部相互作用机制，从而揭示出群集运动现象的本质。\par
本文运用一种新型的机器学习算法-稀疏贝叶斯学习对鸽群运动的时序数据进行网络辨识，从中揭示出了鸽群运动行为互动网络特征和时空特性。基于这一结果，运用动力学相关知识，构建了自驱动粒子模型，通过对模型中相互作用的非线性程度和系统噪声的分析，实现了粒子间的演化与协同控制，在一定程度上解释了鸽群运动的演化机理与相互作用机制。
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\vskip 1cm \noindent{\kaishu \textbf{关键词:}\ 稀疏贝叶斯学习，\ 群集运动，\ 自驱动粒子模型，\ 机器学习}
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Collective Motion phenomenon is common in nature, such as fish school, bird flocks, insects and even bacterial colonies. Recently, the collective motion has attracted extensive research by biologists, physicists, computer scientists and system control scientists with its unique charm. Due to the limitation of data acquisition technology, the collection of group motion data is very difficult, and the early hypothesis-based group behavior model research can not reveal the essential mechanism of life group behavior in nature. Therefore, it is urgent to carry out data analysis and data mining with the empirical data obtained by high-precision equipment. With the advent of today's big data and artificial intelligence era, people can obtain a large amount of valuable data more conveniently and quickly. Therefore, the research of data-driven cluster dynamics has become a hot research direction. The purpose of this thesis is to explore the internal interaction mechanism of collective motion, thus revealing the nature of collective motion phenomena.\par
In this thesis, a new machine learning algorithm-sparse Bayesian learning is used to identify the time series data of the pigeons' movement, which reveals the interactive network characteristics and space-time characteristics of the pigeons' motion behavior. Based on this result, using the knowledge of dynamics, a self-driven particle model is constructed. By analyzing the nonlinearity of the interaction and the system noise in the model, the evolution and coordinated control between particles is realized, which explains to some extent. The evolutionary mechanism and interaction mechanism of pigeon movement. \par
\vskip 0.8cm \noindent{\rm \textbf{Key Words:}\ parse Bayesian learning, Group relationship network, Self-driven particle model, Machine learning}
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\section{研究背景与意义}
自然界中的群集运动\cite{1}屡见不鲜，人们每每看到为之着迷。鱼群\cite{2}可以以相当有序的方式向着某一方向游动，当附近有捕食者靠近的时候它们可以像液体一样旋转，快速有序的改变方向来躲避天敌；数百只椋鸟\cite{3}可以作为一个统一群体飞到田地，然后返回栖息地；细菌可以进行迁徙生长\cite{4}，最终形成肉眼可见的菌落。从宏观的鱼群、鸟群，到微观的细菌，这一切都是群集运动的很好体现。群集运动以其独特的魅力引起了科学家们的广泛关注。科学家们希望通过研究，发掘出群体内部的相互作用机制，通过科学的手段来解释群集运动这一有趣的生物学现象。\par
细心观察图\ref{p14}不难发现，这些群体在运动过程中表现出高度有序、复杂、智能化的行为模式，似乎在这些群集中存在一种潜在的“群集意识”，而这种“群集意识”是通过自组织来实现的\cite{5}。这里所说的自组织是指个体不受外界操控，与群体中的局部个体进行信息交互，最终与群体形成协调一致的运动过程。群体中每个个体进行自组织运动，最终形成涌现现象。所谓涌现是指个体间的相互作用，使得群体中产生未经事先规划而真实发生的行为。通过自组织和涌现可以更加直观的理解群集运动现象\cite{5}。群体之所以产生这种自组织的涌现行为，与群体所处的自然环境密切相关。科学家最早通过研究鱼群和鸟群的群集运动时发现，动物为了生存进行迁徙、捕食和躲避天敌的过程中，以集体的形式进行，为了避免与其他个体发生相撞以及掉队的情况发生，最终形成自组织的群集运动的现象。\par
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