Multilayer feedforward networks are universal approximators
Neural Networks
Self-organizing maps
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
Circular backpropagation networks for classification
IEEE Transactions on Neural Networks
Artificial neural networks for feature extraction and multivariate data projection
IEEE Transactions on Neural Networks
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
Dynamic Growing Self-organizing Neural Network for Clustering
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
A neural network approach to study o3 and PM10 concentration in environmental pollution
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
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Environmental data sets are characterized by a huge amount of heterogeneous data from external fields. As the number of measured points grows, a strategy is needed to select and efficiently analyze the useful information from the whole data set. One efficient way of obtaining the validation-compression of data sets is the adoption of a restricted set of features that describe, with an assigned accuracy a subset of the whole data set. One characteristic feature of the environmental data is time dependency: in the medium and long term they are not stationary data sets. The aim of this work is to propose a feature extraction technique based on a new model of an unsupervised neural network suitable to analyze this kind of data. The paper reports the results obtained utilizing the above extraction and analysis procedure on a real data set on chemical pollutants. It is shown that the proposed neural network is able to identify correctly human and/or meteorological effects in the environmental data set.