Neural Spatial Interaction Predictors and Pattern Detectors

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和创造性任务（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本质上是并行的，它们将知识分布在整个系统中，而不是将其集中在特定的位置，它们具有容错能力，能够从不完整、嘈杂或不精确的信息中学习并做出决策。由于这些特点，人工神经网络在未来处理地理信息方面具有巨大的潜力，但这种潜力尚未得到地理学家和区域科学家的广泛探索。一位美国地理学家将与一位奥地利合作者合作，探索人工神经网络在地理研究中的一些潜在用途。本项目的重点将放在空间相互作用研究的应用上。应用相关的神经空间交互预测器将被设计、实现和测试，基于无监督人工神经网络的交互模式检测器也将被设计、实现和测试。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，通过对奥地利区域间电话数据的实证检验。该项目是对使用人工神经网络方法分析地理模式和过程的潜力的探索性研究。通过关注空间相互作用，研究人员将探索一个传统方法已经建立的重要主题的新的分析视角。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研究中受益，这是一种解决问题的创新方法。＊＊＊

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. ***