地理学报 ›› 2006, Vol. 61 ›› Issue (6): 663-672.doi: 10.11821/xb200606010

• 资源与地理信息系统 • 上一篇    下一篇

从高维特征空间中获取元胞自动机的非线性转换规则

刘小平, 黎夏   

  1. 中山大学地理科学与规划学院,广州510275
  • 收稿日期:2005-08-30 修回日期:2005-12-07 出版日期:2006-06-25 发布日期:2010-09-06
  • 通讯作者: 黎夏, 男, 教授, 博导, 从事遥感和地理信息系统研究。E-mail: lixia@mail.sysu.edu.cn E-mail:lixia@mail.sysu.edu.cn
  • 作者简介:刘小平(1978-),男,湖南邵阳人,博士生,主要从事定量遥感和地理信息系统模型研究。E-mail: yiernanh@163.com
  • 基金资助:

    国家杰出青年基金项目 (40525002); 国家自然科学基金项目 (40471105); “985工程”GIS与遥感的地学应用科技创新平台项目 (105203200400006)

Retrieving CA Nonlinear Transition Rule from High-dimensional Feature Space

LIU Xiaoping, LI Xia   

  1. School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
  • Received:2005-08-30 Revised:2005-12-07 Online:2006-06-25 Published:2010-09-06
  • Supported by:

    National Outstanding Youth Foundation of NSF of China, No.40525002; National Natural Science Foundation of China, No.40471105; “985 Project”of GIS and Remote Sensing for Geosciences from the Ministry of Education of China, No.105203200400006

摘要:

元胞自动机 (CA) 具有强大的空间模拟能力,能够模拟和预测复杂的地理现象演变过程。CA 的核心是如何定义转换规则,但目前CA转换规则获取往往是基于线性方法来进行,例如采用多准则判断 (MCE) 技术。这些方法较难反映地理现象所涉及的非线性等复杂特征。为此提出了利用新近发展的核学习机来获取地理元胞自动机非线性转换规则的新方法。该方法是通过核函数产生隐含的高维特征空间,把复杂的非线性问题转化成简单的线性问题,为解决复杂非线性问题提供了一种非常有效的途径。利用所提出的方法自动获取地理元胞自动机的转换规则,不仅大大减少了建模所需的时间,也较好地反映地理现象复杂的特性,从而改善了CA模拟的效果。

关键词: 元胞自动机, 转换规则, 非线性, 核学习机, 高维特征空间

Abstract:

Cellular Automata (CA) has strong spatial modeling capabilities, which can simulate the evolution of complex geographical phenomena. The core of CA models is how to define transition rules that control the conversion of states in simulation. Transition rules of CA models are usually defined using linear methods, such as multicriteria evaluation (MCE). However, the evolution of geographical phenomena often manifests the complexity of nonlinear features. Discrepancy can be produced by just using the linear solution for retrieving transition rules. This paper proposes a new method to acquire nonlinear transition rules of CA by using the techniques of kernel-based learning machines. The method can transform complex nonlinear problems to simple linear problems through an implicit high-dimensional feature space which is produced by kernel functions. This study has demonstrated that the proposed method can effectively solve complex nonlinear problems in simulating geographical phenomena. It has been applied to the simulation of urban expansion in the fast growing city, Guangzhou. Comparison indicates that more reliable simulation results can be generated by this method.

Key words: cellular automata, transition rule, nonlinear, kernel-based learning machine, high-dimensional feature space