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随着对GIS中的空间对象模型和自然地理特征表达的研究深入,模糊空间对象被提出。针对模糊空间对象表达的特点,提出了一种基于模糊神经网络的模糊空间对象生成方法。该方法将模糊技术与神经网络相结合,利用神经网络的学习能力调整模糊隶属函数和模糊规则,使系统具备自适应的特性。实验表明,这种基于模糊神经网络的生成模糊空间对象的方法比传统方法大大的提高了成果的精度。 相似文献
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随着对GIS中的空间对象模型和自然地理特征表达的研究深入,模糊空间对象被提出。针对模糊空间对象表达的特点,提出了一种基于模糊神经网络的模糊空间对象生成方法。该方法将模糊技术与神经网络相结合,利用神经网络的学习能力调整模糊隶属函数和模糊规则,使系统具备自适应的特性。实验表明,这种基于模糊神经网络的生成模糊空间对象的方法比传统方法大大的提高了成果的精度。 相似文献
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将模糊关联规则挖掘方法与模糊空间概念层次表达、模糊空间关系层次分析等结合起来,研究模糊空间关联规则挖掘的理论和方法.对于挖掘算法以及规则的置信度和隶属度计算问题,文中结合应用实际,给出了详细理论推演和算法实现过程. 相似文献
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针对土地利用数据综合中图斑的化简处理这一复杂问题,把智能体技术引入到土地利用图斑的处理,建立了土地利用图斑综合的多智能体系统,包括系统的知识库、状态诊断、上下文环境探测、智能决策过程以及土地利用图斑智能体的动作执行和运行结果评价,并通过实际生产进行检验. 相似文献
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将模糊关联规则挖掘方法与模糊空间概念层次表达、模糊空间关系层次分析等结合起来，研究模糊空间关联规则挖掘的理论和方法。对于挖掘算法以及规则的置信度和隶属度计算问题，文中结合应用实际，给出了详细理论推演和算法实现过程。 相似文献
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土地利用规划是土地利用管理的前提,针对土地利用规划群体决策的需要,在现行规 划特性分析基础上,提出了一种基于Multi-Agent的土地利用规划决策支持系统,简 要探讨了该系统的体系结构,各Agent的功能和主要决策算法,以及系统实现关键技 术,并利用面向Agent软件设计方法开发了一个原型系统,较好地实现了土地利用规 划的辅助决策。 相似文献
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把细胞自动机和灰色局势决策结合起来对土地利用变换机制进行模拟。实验证明,基于灰色局势决策规则的细胞自动机是对土地利用变换机制从宏观和微观角度进行模拟的有效方法。 相似文献
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土地利用变化研究中的细胞自动机与灰色局势决策 总被引：12，自引：0，他引：12
把细胞自动机和灰色局势决策结合起来对土地利用变换机制进行模拟。实验证明,基于灰色局势决策规划的细胞自动机是对土地利用变换机制从宏观和微观角度进行模拟的有效方法。 相似文献
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利用数字化技术，结合水利学、模糊控制理论，讨论了如何建立渠道运行自动控制系统，并建立了渠道运行的自动控制数学模型，设计出模糊控制系统，提出了一套相应的模糊规则、隶属度函数等。最后将系统以实际情况进行模拟、仿真实现，为渠道自动控制系统的稳定运行与决策提供重要数据和依据。 相似文献
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为解决高分辨率遥感影像分割中,由光谱测度的空间复杂性、相同类型地物目标异质性增大带来的类属不确定性以及分割决策不确定性等引起的分割精度下降问题,提出一种融入空间关系的区间二型模糊模型高分辨率遥感影像监督分割方法。(1)建立高斯函数模型作为一型模糊模型,用来刻画像素类属的不确定性;(2)模糊化一型模糊模型中的均值或标准差,建立区间二型模糊模型,以强化类属的不确定表达和增加分割决策信息;(3)综合一型模糊模型及区间二型模糊模型的上、下隶属函数建模模糊决策模型;(4)融入邻域像素关系,使用待分像素及其邻域像素在模糊决策模型中的隶属度共同决定像素的类属。采用本文算法分别对真实高分辨遥感影像及合成影像进行分割,并对测试结果进行定性和定量分析。结果表明,本文算法可以得到更高的分割精度。 相似文献
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土地适宜性评价的模糊神经网络模型 总被引：19，自引：2，他引：19
基于神经网络来构造模糊系统，建立了土地适宜性评价的模糊神经网络模型；根据神经网络误差反向修正的原理，设计和推导了该模型的学习算法。实验结果表明，该模型应用于土地适宜性评价具有高效、客观、准确等优点。 相似文献
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This article compares two fuzzy approaches to land suitability evaluations, Analytical Hierarchy Process (AHP) and Ideal Point. The methods were evaluated using a case study which models the opportunities for wheat production under irrigation conditions in the north‐western region of Jeffara Plain, Libya. A number of relevant soil and landscape criteria were identified through a review of the literature and their weights specified as a result of discussions with local experts. The results of the Fuzzy AHP showed that the majority of the study area has membership values to the set of suitability between 0.40 and 0.50, while the results of the Ideal Point approach revealed most of the study area to have membership values between 0.30 and 0.40. While the Fuzzy AHP and Ideal Point approaches accommodate the continuous nature of many soil properties and produce more intuitive distributions of land suitabilities values, the Fuzzy AHP approach was found to be better than Fuzzy Ideal Point. This was due to the latter's tendency to be biased towards positive and negative ideal values. 相似文献
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针对高分辨率遥感影像分类中由于细节特征突出、同质区域光谱测度变异性增大所带来的像素类属的不确定性及模型的不确定性等造成的误分结果,提出一种基于模糊隶属函数的监督分类方法。对同质区域定义高斯隶属函数模型用来表征像素类属不确定性;模糊化该隶属函数参数建立影像模糊隶属函数,以建模同质区域光谱测度的不确定性;用训练样本在所有类别中的模糊隶属函数及原隶属函数（高斯隶属函数）中的隶属度为输入,建立模糊线性神经网络模型作为目标函数,实现分类决策。该算法和经典算法对World View-2全色合成影像及真实影像进行定性和定量分类实验,分类结果验证了文中方法具有更高的分类精度。 相似文献
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Evaluating Errors in a Digital Vegetation Map with Forest Inventory Data and Accuracy Assessment Using Fuzzy Sets 总被引：1，自引：0，他引：1
Janet Franklin Dena K. Simons Debby Beardsley John M. Rogan & Hazel Gordon 《Transactions in GIS》2001,5(4):285-304
In large-area mapping projects, existing reference data, often collected for a different purpose, are increasingly being used for map accuracy assessment. Multi-attribute digital vegetation maps have been developed for all National Forest lands in California (8.1 million ha). We developed decision rules that could be applied to quantitative Forest Inventory and Analysis (FIA) plot data in order to score the fuzzy membership of plot locations in all possible map classes. We compare the accuracy of the vegetation map attributes estimated using this method to accuracy estimated from fuzzy class membership scores assigned by experts (inventory crews) during field work. Accuracy of the vegetation life form attribute was estimated to be higher when expert label assignments were used as reference data (76–87%), instead of FIA plot data (62–79%). This suggests that automated decision rules applied to detailed data from FIA plots, which have smaller area than map polygons, may systematically underestimate map accuracy. However, assignment of the actual map labels to FIA plot locations by inventory crews appears to be a robust method for using the FIA data for accuracy assessment. 相似文献
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National borders play an important role in everyday life. Interest in border studies has increased with recent changes in geographical locations of the border or the fluctuation of the permeability of the border between some countries, such as in the European Union. Whether the nations are trying to increase traffic flow of the border or to implement stricter border control, having appropriate information of the border is crucial for effective policymaking. The objective of this research was to identify areas of high porosity, or high permeability, for pedestrians along the southern national border region in Carinthia, Austria using terrain, land use, and road data along with geocomputational methods. Two unsupervised classification methods, the fuzzy K-means clustering and the Self-Organizing Map, were applied to segment the border into homogeneous zones according to topographic and infrastructural attributes. The fuzzy K-means clustering method was chosen for its ability to allow for a continuous approach to classification. With this method, an object can belong, with different degrees of membership, to multiple classes, which is a more realistic reflection of the natural world than discrete clustering, where each object can only belong to one class. However, the fuzzy K-means clustering method does have disadvantages, i.e. the user must determine the number of classes and the input parameters are required to be in continuous format. The second classification method, the Self-Organizing Map, is a type of artificial neural network and was chosen for its ability to automatically determine the number of classes and handle categorical data. The Self-Organizing Map is unique because it can transform high dimensional data into low dimensional display while preserving the topology and spatial distribution of the input parameters. The results of the two classification methods suggest that the fuzzy K-means classification is more effective than the Self-Organizing Map for this situation. However, more research is needed to determine the fit of these algorithms for particular spatial data classification tasks. The results obtained from this research provide an insight into the permeability of the border region of Carinthia, Slovenia, and Italy to pedestrian traffic and can be potentially useful for decision making processes for tourism development and road transportation management in that region. Furthermore, the approach presented in this article can be applied to other national borders to identify zones permeable to pedestrian traffic. 相似文献
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D.G. StavrakoudisJ.B. Theocharis G.C. Zalidis 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(4):529-544
A Boosted Genetic Fuzzy Classifier (BGFC) is proposed in this paper, for land cover classification from multispectral images. The model comprises a set of fuzzy classification rules, which resemble the reasoning employed by humans. Fuzzy rules are generated in an iterative fashion, incrementally covering subspaces of the feature space, as directed by a boosting algorithm. Each rule is able to select the required features, further improving the interpretability of the obtained model. After the rule generation stage, a genetic tuning stage is employed, aiming at improving the cooperation among the fuzzy rules, thus increasing the classification performance attained after the first stage. The BGFC is tested using an IKONOS multispectral VHR image, in a lake-wetland ecosystem of international importance. For effective classification, we consider advanced feature sets, containing spectral and textural feature types. Comparative results with well-known classifiers, commonly employed in remote sensing tasks, indicate that the proposed system is able to handle multi-dimensional feature spaces more efficiently, effectively exploiting information from different feature sources. 相似文献
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基于计算智能的土地适宜性评价模型 总被引：22，自引：2，他引：22
将计算智能理论引入土地评价领域，构建了一个全新的土地适宜性评价模型。首先基于模糊逻辑和人工神经网络构造了一个模糊神经网络模型，然后采用改进的遗传算法进行训练，能够快速收敛到最优解，对初始的规则库进行修正，形成了一个自学习、自适应的评价系统。 相似文献