Neural networks for enhanced human-computer interactions

摘要

The use of neural networks to create adaptive models of users and communications channels for use in designing system response characteristics is discussed. Two types of neural networks that will be useful for this type of task are considered. One, the Kohonen learning vector quantization (LVQ) network, is a clustering network. It can adjust the vector element values of a set of quantizing vectors in order to create exemplar vectors which represent clusters in a set of data. The other, the Kohonen self-organizing topology-preserving map (SOTPM), is a more advanced and powerful network that uses similar principles as the LVQ. It can create a topographic mapping of a set of vector data which creates a data clustering visible in a reduced dimensionality space from the original data. This will facilitate interpretation of the data clusters describing different user types. The use of a rather highly evolved form of neural networks to create more powerful system models is discussed.