Adaptive Information System for Data Approximation Problems
Witold Kosiński, Martyna Weigl · 2000
An adaptive information system is constructed for the approximation of a multidimensional data base. Such a construction problem can be regarded as an approximation of a multivariate real-valued function that is known in a discrete number of points. That set of points forming the multidimensional data base is called a training set TRE for the adaptive information system. The constructed system contains a module of one-conditional fuzzy rules consequent parts of which are artificial feed-forward neural networks. The numerical data contained in TRE are used to construct premise parts of each rule and membership functions of fuzzy sets involved in them. They are specified with the help of a clustering analysis of TRE. For each fuzzy rule the neural network appearing as its consequent part is trained on the corresponding cluster as on its subdomain In that way the cluster analysis can supply a knowledge in designing the approximator. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.