Với sự ra đời của cơ sở dữ liệu không gian lớn, đôi khi bao gồm terabyte dữ liệu, phương pháp truyền thống của số liệu thống kê như mô tả trong chương trước trở nên không đứng vững. | GEOCOMPUTATION 79 on Spatial Information Theory COSIT series is to a large degree devoted to the development of methods of qualitative spatial reasoning unfortunately not much of the work presented there 1993-2005 has made it into readily available software. Neural networks With the advent of large spatial databases sometimes consisting of terabytes of data traditional methods of statistics such as those described in the previous chapter become untenable. The first group of GIScientists to encounter that problem was remote sensing specialists and so it is no surprise that they were the first to discover neural networks as a possible solution. Neural networks grew out of research in artificial intelligence where one line of research attempts to reproduce intelligence by building systems with an architecture that is similar to the human brain Hebb 1949 . Using a very large number of extremely simple processing units each performing a weighted sum of its inputs and then firing a binary signal if the total input exceeds a certain level the brain manages to perform extremely complex tasks see Figure 64 . Feature vector Threshold effect described as Figure 64 Schematics of a single neuron the building block of an artificial neural network Using the software sometimes though rarely hardware equivalent of the kind of neural network that makes up the brain artificial neural networks accomplish tasks that were previously thought impossible for a computer. Examples include adaptive learning self-organization error tolerance real-time operation and parallel processing. As data is given to a neural network it re- organizes its structure to reflect the 80 KEY CONCEPTS AND TECHNIQUES IN GIS properties of the given data. In most neural network models the term self-organization refers to the determination of the connection strengths between data objects the so-called neurons. Several distinct neural network models can be distinguished both from their internal architecture and
