基于神经网络的单元自动机CA及真实和忧化的城市模拟   总被引：6，自引：0，他引：6  

黎夏  叶嘉安 《地理学报》2002,(2)

提出了一种基于神经网络的单元自动机（ＣＡ）。ＣＡ已被越来越多地应用在城市及其它地理现象的模拟中。ＣＡ模拟所碰到的最大问题是如何确定模型的结构和参数。模拟真实的城市涉及到使用许多空间变量和参数。当模型较复杂时，很难确定模型的参数值。本模型的结构较简单，模型的参数能通过对神经网络的训练来自动获取。分析表明，所提出的方法能获得更高的模拟精度，并能大大缩短寻找参数所需要的时间。通过筛选训练数据，本模型还可以进行优化的城市模拟，为城市规划提供参考依据。  相似文献   

A study on the early-warning technique concerning debris flow disasters     

ZHOU Jinxing  Wang Lixian  XIE Baoyuan  FEI Shimin  Wang Xilin 《地理学报(英文版)》2002,12(3)

According to the principle of the eruption of debris flows, the new torrent classification techniques are brought forward. The torrent there can be divided into 4 types such as the debris flow torrent with high destructive strength, the debris flow torrent, high sand-carrying capacity flush flood torrent and common flush flood by the techniques. In this paper, the classification indices system and the quantitative rating methods are presented. Based on torrent classification, debris flow torrent hazard zone mapping techniques by which the debris flow disaster early-warning object can be ascertained accurately are identified. The key techniques of building the debris flow disaster neural network (NN)real time forecasting model are given detailed explanations in this paper, including the determination of neural node at the input layer, the output layer and the implicit layer, the construction of knowledge source and the initial weight value and so on. With this technique, the debris flow disaster real-time forecasting neural network model is built according to the rainfall features of the historical debris flow disasters, which includes multiple rain factors such as rainfall of the disaster day, the rainfall of 15 days before the disaster day, the maximal rate of rainfall in one hour and ten minutes. It can forecast the probability, critical rainfall of eruption of the debris flows, through the real-time rainfall monitoring or weather forecasting. Based on the torrent classification and hazard zone mapping, combined with rainfall monitoring in the rainy season and real-time forecasting models, the debris flow disaster early-warning system is built. In this system, the GIS technique, the advanced international software and hardware are applied, which makes the system′s performance steady with good expansibility. The system is a visual information system that serves management and decision-making, which can facilitate timely inspect of the variation of the torrent type and hazardous zone, the torrent management, the early-warning of disasters and the disaster reduction and prevention.  相似文献   

Competitive Adjustment and Advancement in Global Commodity Chains II: The Case of the Singapore Garment Industry     

Leo van Grunsven  & Floor Smakman 《Singapore journal of tropical geography》2002,23(1):70-92

This is the second part of a contribution to the debate on the possibilities of leveraging economic globalisation — through incorporation in international production networks and global commodity chains (GCCs) — as a strategy for developing countries to industrialise and advance their position in the world economy. In the first part, we analysed the experience of the East Asian apparel industry and addressed the issues of sustaining positions, upgrading to more rewarding roles, and advancing to less dependent positions within production networks and GCCs. We developed an analytical framework at both the firm and industry levels, and subsequently identified alternative firm- and industry-level strategies and trajectories. The present paper deals with these issues in the context of the Singapore apparel industry. Based on extensive empirical research, we demonstrate that although the East Asian experience of upgrading and repositioning within the GCC is to some extent emulated in the case of the Singapore apparel industry, the outcomes have been less favourable in terms of the depth, extent and strength of these trajectories. The differing outcomes can be explained in terms of different (systemic) conditions in the Singapore business environment, including the agency of local players, the geography of sourcing networks, and the role of the state and prevailing business attitudes. Our conclusions merit continued attention in both research and policy circles on the development of capabilities at the firm level, and the role of local business and institutional environments in local industry development processes under globalisation.  相似文献   

Use of Noise to Augment Training Data: A Neural Network Method of Mineral–Potential Mapping in Regions of Limited Known Deposit Examples     

Warick M. Brown  Tamás D. Gedeon  David I. Groves 《Natural Resources Research》2003,12(2):141-152

One of the main factors that affects the performance of MLP neural networks trained using the backpropagation algorithm in mineral-potential mapping isthe paucity of deposit relative to barren training patterns. To overcome this problem, random noise is added to the original training patterns in order to create additional synthetic deposit training data. Experiments on the effect of the number of deposits available for training in the Kalgoorlie Terrane orogenic gold province show that both the classification performance of a trained network and the quality of the resultant prospectivity map increasesignificantly with increased numbers of deposit patterns. Experiments are conducted to determine the optimum amount of noise using both uniform and normally distributed random noise. Through the addition of noise to the original deposit training data, the number of deposit training patterns is increased from approximately 50 to 1000. The percentage of correct classifications significantly improves for the independent test set as well as for deposit patterns in the test set. For example, using ±40% uniform random noise, the test-set classification performance increases from 67.9% and 68.0% to 72.8% and 77.1% (for test-set overall and test-set deposit patterns, respectively). Indices for the quality of the resultant prospectivity map, (i.e. D/A, D × (D/A), where D is the percentage of deposits and A is the percentage of the total area for the highest prospectivity map-class, and area under an ROC curve) also increase from 8.2, 105, 0.79 to 17.9, 226, 0.87, respectively. Increasing the size of the training-stop data set results in a further increase in classification performance to 73.5%, 77.4%, 14.7, 296, 0.87 for test-set overall and test-set deposit patterns, D/A, D × (D/A), and area under the ROC curve, respectively.  相似文献   

Use of Fuzzy Membership Input Layers to Combine Subjective Geological Knowledge and Empirical Data in a Neural Network Method for Mineral-Potential Mapping   总被引：3，自引：0，他引：3  

Brown  Warick  Groves  David  Gedeon  Tamás 《Natural Resources Research》2003,12(3):183-200

Use of GIS layers, in which the cell values represent fuzzy membership variables, is an effective method of combining subjective geological knowledge with empirical data in a neural network approach to mineral-prospectivity mapping. In this study, multilayer perceptron (MLP), neural networks are used to combine up to 17 regional exploration variables to predict the potential for orogenic gold deposits in the form of prospectivity maps in the Archean Kalgoorlie Terrane of Western Australia. Two types of fuzzy membership layers are used. In the first type of layer, the statistical relationships between known gold deposits and variables in the GIS thematic layer are used to determine fuzzy membership values. For example, GIS layers depicting solid geology and rock-type combinations of categorical data at the nearest lithological boundary for each cell are converted to fuzzy membership layers representing favorable lithologies and favorable lithological boundaries, respectively. This type of fuzzy-membership input is a useful alternative to the 1-of-N coding used for categorical inputs, particularly if there are a large number of classes. Rheological contrast at lithological boundaries is modeled using a second type of fuzzy membership layer, in which the assignment of fuzzy membership value, although based on geological field data, is subjective. The methods used here could be applied to a large range of subjective data (e.g., favorability of tectonic environment, host stratigraphy, or reactivation along major faults) currently used in regional exploration programs, but which normally would not be included as inputs in an empirical neural network approach.  相似文献   

企业网络发育程度与区域创新能力研究   总被引：10，自引：0，他引：10  

文嫮  李小建 《世界地理研究》2003,12(2):39-46

本文从分析企业网络的发育和完善程度出发，探讨影响网络创新能力的因素。文章指出，网络发育程度与创新能力(创新数量、创新发生的频率)是正相关的。从宏观上讲网络越密集、各种正式和非正式联系越有效、网络联系越稳定、网络自我更新能力越强、网络的开放性越强、网络越能根植于当地的良好的区域环境，越容易激发创新；从微观上讲，网络中行为主体的学习能力越强，其创新发生频率越高，创新能力越强。  相似文献   

Sensitivity Analysis of a Feedforward Neural Network for Considering Genetic Mechanisms of Kuroko Deposits     

Matsuda  Setsuro  Koike  Katsuaki 《Natural Resources Research》2003,12(4):291-301

Exploration for volcanogenic massive sulfide deposits of the kuroko-type is underway in many places. Clarifying the spatial patterns of the metals in kuroko deposits will be useful for understanding their genetic mechanisms and for future exploration of such types of deposits. This study represents a spatial distribution analysis on the contents of principal metals of kuroko deposits: Cu, Pb, and Zn, in the Hokuroku district, northern Japan, by a feedforward neural network and 1917 sample data at 143 drillhole sites. The network, which consists of three layers, was trained by the principle of SLANS in which the numbers of neurons in the middle layer and training data are changed to improve estimation accuracy. Using the weight coefficients connecting adjacent neurons, sensitivity analysis of the neural network was carried out to identify factors influencing spatial distributions of the three metals. The coordinates depth (z) direction, Bouguer gravity, and specific lithology such as dacite were determined to be influencing factors. The high frequency of the z coordinate signifies that the metal contents differ to a large extent by depth. The sensitivity vector was defined using sensitivity coefficients for x, y, and z coordinates of an estimation point. We determined that the directions of large vectors were different inside and outside of the Hanawa-Ohdate area. This characteristic is considered to originate from the differences in the permeability of fractures that became the paths for rising ore solutions, and the depths that the solutions mixed with sea water.  相似文献   

红板岩材料隐式本构模型的建立及其应用研究     

邹光华  朱建明 《工程地质学报》2003,11(3):258-262

针对红板岩材料在岩土工程中所表现的大量模糊的和不确定的因素等特点,基于人工神经网络的学习能力,借助于室内岩石力学试验,进行了对该材料的力学本构特性进行了神经网络模拟研究,提出了隐式本构模型的思想和方法,并通过该方法对该岩石的流变试验结果进行学习,获得了以网络权值结构保存的力学特性知识,由此得到了表征红板岩应力应变本构关系的隐式本构模型。应用结果表明,该方法对岩土类材料本构关系的模拟研究具有很好的应用前景。  相似文献   

神经网络技术在地面沉降区划中的应用研究   总被引：1，自引：2，他引：1  

武建强  余勤 《江苏地质》2003,27(3):171-174

复杂的第四系沉积环境构成地面沉降的地质背景，在对比分析第四系厚度、地下水位、含水层以及软土层发育程度的基础上，尝试性地运用人工神经网络技术对苏锡常地区地面沉降易发区进行识别。除基岩出露区外全区共被划分成4类块段，这种分布与第四系条件具有很强的空间相关性，经检验具有较高的可信度。  相似文献   

基坑支护结构水平变形预测的遗传神经网络方法   总被引：2，自引：2，他引：2  

韦立德  徐卫亚  蒋中明  刘世君  陈记 《工程地质学报》2003,11(3):297-301

采用遗传算法和误差反向传播算法相结合的混合算法来训练前馈人工神经网络,先用遗传学习算法进行全局训练,再用BP算法进行精确训练。就遗传算法过程中的选择、变异进行了探索,提出了用BP网络训练产生变异的遗传算法。作为实例,将该方法应用于预测基坑支护结构水平变形中。结果表明,该方法有收敛速度较快、预测精度高等优点。  相似文献