【作者】 孙燕楠；

【导师】 韩敏；

【作者基本信息】 大连理工大学 ， 控制理论与控制工程， 2007， 博士

【摘要】 湿地时空格局的动态演变模拟有利于监测和分析湿地格局的变化，可以形象地辅助管理湿地资源。目前，湿地格局变化的研究多为景观指数计算分析，较少反映格局的时空特性，不能检测具体位置的变化过程，因此难以实施更具针对性的管理措施。而模拟湿地时空格局的动态演变恰好可以弥补景观指数分析的不足。本文针对湿地格局变化以及已有时空数据的特点，集成神经网络、模糊逻辑、粗集理论等多种智能方法，构建细胞自动机模型，来模拟湿地时空格局演变。主要研究内容和结果如下：(1)因为细胞自动机具有时空特性，能够模拟复杂系统的演化过程，所以本文以细胞自动机为框架，构建了湿地时空格局演变模型。集成多种智能方法，自动实现微观状态转化和获取宏观调节规则。本文将相同状态细胞的质心位置移动作为一种宏观调节规则，引导细胞状态转化的方向，弥补了已有的宏观规则只是限制各个状态的细胞数量的不足。为了有效验证所提模型结果，本文提出综合性比较方法，不仅考虑到像素点的相似性，还考虑了斑块形状、排列对格局的影响。(2)针对湿地时空格局变化的复杂性和监测数据的模糊性、多尺度的特点，构建基于样本分布特性的Takagi-Sugeno模糊神经网络，实现细胞自动机的微观状态转化。该T-S模糊神经网络基于样本的分布特性，自动确定变量语言值的隶属度函数，合理划分模糊空间。根据所确定的隶属度函数，模糊化聚类中心来确定初始规则，并通过聚合方法得到带有重要度的最终规则集。基于所得规则集的前件，连接模糊化层和推理层，简化网络结构。仿真结果表明该网络比已有T-S模糊神经网络具有更高的模拟精度和更好的泛化能力。(3)针对湿地社会经济资料缺乏以及难以建立公式获取数值宏观规则的问题，构建粗集-神经网络模型，通过约简和模拟规则集获取语言值表达的宏观规则，来调节细胞自动机中微观状态转化可能性的大小。为了提高粗集所得规则的泛化能力，本文提出用参数表示隶属度函数的方法，将每个规则的前、后件语言值作为输入、输出数据，采用三层前向网络实现规则集。与常用的隶属度函数向量表示法、区间表示法相比，这种方法使得网络训练速度更快，对待识样本的推理结果更准确。(4)模拟和预测扎龙湿地时空格局演变。首先对扎龙湿地1986年-2002年的格局进行指数分析，挖掘促使其格局变化的驱动因子。其次以1986年-1999年已有的扎龙湿地格局为基础，利用湿地时空格局演变模型预测2000年-2002年的格局变化。同时修正模拟城镇扩展的两种细胞自动机模型，也用来预测扎龙湿地的时空格局演变。结果表明，本文所提模型比修正模型更精确地预测出扎龙湿地的格局演变，平均预测精度约为70％，与目前细胞自动机模拟城镇扩展的精度相当。综上，本文构建了基于细胞自动机的湿地时空格局演变模型：提出基于样本分布特性的T-S模糊神经网络，用来计算细胞自动机微观状态转化的可能性；集成粗集理论和神经网络构建了粗集-神经网络模型，用此模型获取语言值表示的宏观规则，来调节微观状态转化的可能性。将该模型应用于扎龙湿地格局演化，取得了良好的预测效果。更多还原

【Abstract】 The simulation of spatio-temporal evolution of wetlands is useful for monitoring and analyzing wetland pattern changes. It can vividly assist in managing the wetland resources. Recently most research of wetland pattern changes has focused on landscape indices analysis. The spatio-temporal characteristics of wetlands are seldom represented and the change process in the specific location cannot be monitored, thus, it is difficult to carry on a pertinent management plan. A spatio-temporal evolution simulation can make up the deficiency of the landscape indices analysis. Considering the characteristics of wetland pattern changes and spatio-temporal data, a cellular automaton (CA) model is established to integrate intelligent methods, including neural network, fuzzy logic and rough set to simulate the spatio-temporal evolution of wetlands. The main methods and results are shown as follows.(1) Because CA has spatio-temporal characteristics and it can simulate the evolution of complex systems, a spatio-temporal evolution of wetlands (STEW) model based on CA is proposed. This model integrates some intelligent modeling methods to realize micro-state transition of CA and acquire macro-adjusting rules automatically. In the model the centroid point movement of cells with the same state is chosen as a macro-adjusting rule. It can lead state change of cells and offsets the limitation of the quantitative macro-adjusting rule, which jusf constrains the number of cells of every state. To evaluate the results of CA reasonably, a comprehensive comparison method is proposed. In the method not only the pixel to pixel comparison but also the influences of the shape and array of patches to the pattern are considered.(2) The wetland pattern changes are complex and the data monitoring wetlands are fuzzy and multi-scale, so a Takagi-Sugeno fuzzy neural network based on samples distribution characteristics (TSFNN-SDC) is put forward to complete the micro-state transition. Membership functions of linguistic values of variables are obtained automatically based on the distribution characteristics of samples. So the fuzzy space is partitioned reasonably. The clustering centers are fuzzified based on the membership functions to get initial rules and the same rules merge into one. In effect, the final rules with their degrees of importance are obtained. According to the antecedent conditions of the rules the fuzzification layer and inference layer are connected so that the structure of network is simplified. Simulation results indicate that the proposed fuzzy neural network has better simulation accuracy and better generalization ability than traditional T-S fuzzy neural network.(3) Because the socio-economic information of wetlands is absent and it is difficult to design formula to get quantitative macro-adjusting rules, a Rough-Neural-Network (Rough-NN) model is constructed. Rule set are reduced and simulated to acquire the linguistic macro-adjusting rules. By these rules the micro-state transition potentials are adjusted. To improve the generalization of rules obtained by rough set, the paper proposes a parameter-representing membership function method which turns antecedent conditions and consequent conclusions of rules into input and output data respectively, at the same time, a three-layer feedforward neural network is applied to simulate the rules. By the parameter-representing method the network has higher training speed and better reasoning results for testing samples than that of using the usual vector-representing method or interval-representing method.(4) Zhalong wetland is taken as a case study of this STEW model. Landscape indices of landscape pattern of Zhalong wetland in the period of 1986-2002 are calculated and analyzed to explore the driving factors. And then based on the patterns in 1986-1999 STEW model is applied to forecast the spatio-temporal evolution of Zhalong wetland from 2000 to 2002. Meanwhile the two models applied to simulate the expansion of urban are modified to simulate the spatio-temporal evolution of Zhalong wetland. Simulation results show that the proposed STEW model has higher prediction accuracy than the modified models. The mean prediction accuracy with the STEW model is about 70%, which is comparable with urban expansion prediction accuracy obtained by cellular automata recently.In summary, the STEW model based on CA is proposed. In this model, the TSFNN-SDC is designed to calculate the micro-state transforming potentials of CA; and the rough set theory and neural network are integrated to establish the Rough-NN model which is applied to obtain the macro-adjusting rules represented by linguistic values to adjust the potentials. Good prediction effects are achieved when the STEW model is applied to simulate the pattern changes in Zhalong wetland.更多还原