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一种非编程的自适应信息处理形式，称为人工神经网络(ANN)，为执行复杂和创造性的任务(如模式分类和完成以及图像和语音识别)提供了另一种方法。人工神经网络在结构上天生是平行的，它们将知识分布在整个系统中，而不是将知识集中在特定的位置，并且它们具有容错能力，从不完整、有噪音或不精确的信息中学习并做出决策。由于这些特点，人工神经网络为未来在地理信息处理中的应用提供了巨大的潜力，但这种潜力还没有被地理学家和区域科学家广泛开发。与一位奥地利合作者合作，一位美国地理学家将探索人工神经网络在地理研究中的一些潜在用途。本项目的注意力将集中在空间相互作用研究的应用上。将设计、实施和测试依赖于应用的神经空间相互作用预测器，以及基于非监督神经网络的相互作用模式检测器。对这些问题的人工神经网络方法将与传统的统计方法进行比较，方法是用奥地利区域间电话的数据进行实证检验。该项目是对在地理模式和过程分析中使用人工神经网络方法的潜力的探索性展望。通过专注于空间相互作用，研究人员将探索关于一个重要主题的新的分析视角，传统方法对此已有很好的认识。无论这些比较的结果如何，地理学、区域科学、计算机科学和相关领域的科学家都将从这项关于解决问题的创新方法的研究中受益。***