A Neuro-Fuzzy system is structurally analogue to a neural network and can be seen as a network whose nodes act according to fuzzy rules. By means of a learning algorithm, it is possible to define, within the input space, a set of regions of “vague” or “non-certain” classification, each one associated to a fuzzy rule. Neuro-Fuzzy networks, for their intrinsic capability of handling never-processed data, are able to follow certain dynamics that are non-linear such as those of Ozone trend. The aim of our work was analyzing the behaviour of Neuro-Fuzzy models applied to Ozone forecasting. In order to explore the features of the new architecture, several tests have been carried out varying the model complexity both in membership-function number and in the membership function types. For this preliminary study, few Ozone-correlated data (namely Nitrogen Dioxide concentration, solar-radiation intensity and wind speed) represented input data sets. Further tests have been carried out to observe the network sensitivity to input data. Results show how different data series require a different complexity of the model in order to have optimized performances. Our preliminary results are encouraging.
A neuro-fuzzy model for ozone forecasting
COCCI GRIFONI, ROBERTA;PASSERINI, GIORGIO;Tascini, Simone
2005-01-01
Abstract
A Neuro-Fuzzy system is structurally analogue to a neural network and can be seen as a network whose nodes act according to fuzzy rules. By means of a learning algorithm, it is possible to define, within the input space, a set of regions of “vague” or “non-certain” classification, each one associated to a fuzzy rule. Neuro-Fuzzy networks, for their intrinsic capability of handling never-processed data, are able to follow certain dynamics that are non-linear such as those of Ozone trend. The aim of our work was analyzing the behaviour of Neuro-Fuzzy models applied to Ozone forecasting. In order to explore the features of the new architecture, several tests have been carried out varying the model complexity both in membership-function number and in the membership function types. For this preliminary study, few Ozone-correlated data (namely Nitrogen Dioxide concentration, solar-radiation intensity and wind speed) represented input data sets. Further tests have been carried out to observe the network sensitivity to input data. Results show how different data series require a different complexity of the model in order to have optimized performances. Our preliminary results are encouraging.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
