Neural Spatial Interaction Predictors and Pattern Detectors

神经空间交互预测器和模式检测器

• 批准号: 9300633
• 负责人: Sucharita Gopal
• 金额: $ 2.9万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-09-15 至 1995-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9300633&HistoricalAwards=false
• 关键词: Neural; Spatial; Interaction; Predictors; Pattern

9300633 GOPAL A form of non-programmed adaptive information processing called Artificial Neural Networks (ANNs) provides an alternative means for performing complicated and creative tasks like pattern classification and completion as well as image and speech recognition. ANNs are inherently parallel in structure, they distribute knowledge throughout the system rather than concentrating it in specific locations, and they are fault- tolerant, learning from and making decisions based on incomplete, noisy, or imprecise information. Because of these characteristics, ANNs offer great potential for future use in processing of geographic information, but that potential has not been widely explored by geographers and regional scientists. Working with an Austrian collaborator, an American geographer will explore a number of potential uses for ANNs in geographic research. Attention in this project will focus on applications to the study of spatial interaction. Application-dependent neural spatial interaction predictors will be designed, implemented, and tested, as will unsupervised ANN-based interaction pattern detectors. ANN approaches to these problems will be compared with traditional statistical approaches through empirical testing with data on Austrian inter-regional telephone calls. This project constitutes an exploratory look into the potential for using ANN approaches in the analysis of geographical patterns and processes. By focusing on spatial interaction, the investigators will explore new analytical perspectives on an important topic for which conventional approaches are well established. Regardless of the outcomes of these comparisons, scientists in geography, regional science, computer science, and related fields will benefit from this research on an innovative new approach to problem solving. ***

9300633 GOPAL一种称为人工神经网络（ANN）的非编程自适应信息处理形式，为执行复杂而创意的任务（例如模式分类和完成）以及图像和语音识别提供了一种替代手段。 ANN在结构上本质上是平行的，它们在整个系统中分配知识，而不是将其集中在特定位置，并且基于不完整，嘈杂或不精确的信息，可以容忍，学习并做出决策。 由于具有这些特征，ANN为未来处理地理信息提供了巨大的潜力，但是地理学家和地区科学家并未广泛探索这种潜力。 美国地理学家与奥地利合作者合作，将在地理研究中探索ANN的许多潜在用途。 该项目中的注意力将集中于对空间相互作用研究的应用。 将设计，实现和测试与应用程序相关的神经空间交互预测指标，基于无监督的ANN相互作用模式探测器也将设计，实现和测试。 ANN解决这些问题的方法将与传统的统计方法通过经验测试与奥地利区域间电话的数据进行比较。 该项目构成了在分析地理模式和过程中使用ANN方法的潜力的探索性研究。 通过专注于空间互动，研究人员将探讨有关一个重要主题的新分析观点，该观点是为传统方法确定的一个重要主题。 不管这些比较的结果如何，地理，区域科学，计算机科学和相关领域的科学家将受益于这项创新的新方法解决问题的方法。 ***