Kernel-Based Cellular Automata for Urban Simulation
Kernel-Based Cellular Automata for Urban Simulation
复制标题
用于城市模拟的基于内核的元胞自动机
DOI：
10.1109/icnc.2007.456
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发表时间：
2007
期刊：
影响因子：
--
通讯作者：
Tao Liu
中科院分区：
文献类型：
--
作者：
Xiaoping Liu;Xia Li;Bin Ai;Shaokun Wu;Tao Liu
元胞自动机（CA）可用于模拟复杂的城市系统。 CA 的校准对于生成现实的城市模式至关重要。常见的校准过程基于线性回归方法，例如多标准评估。本文提出了一种利用基于核的学习机技术来获取CA非线性转移规则的新方法。基于核的方法通过映射到隐式高维特征空间来提取转移规则，将复杂的非线性问题转化为简单的线性问题。该方法已应用于快速发展的城市广州的城市扩张模拟。比较表明，使用这种基于内核的方法可以生成更可靠的仿真结果。
Cellular automata (CA) can be used to simulate complex urban systems. Calibration of CA is essential for producing realistic urban patterns. A common calibration procedure is based on linear regression methods, such as multicriteria evaluation. This paper proposes a new method to acquire nonlinear transition rules of CA by using the techniques of kernel-based learning machines. The kernel-based approach transforms complex nonlinear problems to simple linear problems through the mapping on an implicit high-dimensional feature space for extracting transition rules. This method has been applied to the simulation of urban expansion in the fast growing city, Guangzhou. Comparisons indicate that more reliable simulation results can be generated by using this kernel-based method.