Modelling multivariate data by neuro-fuzzy systems

This paper proposes an approach for solving multivariate modelling problems with neuro-fuzzy systems. Instead of using selected input variables, statistical indices are extracted to feed the fuzzy controller. The original input space is transformed into an eigenspace. If a sequence of training data are sampled in a local context, a small number of eigenvectors which possess larger eigenvalues provide a good summary of all the original variables. Fuzzy controllers can be trained for mapping the input projection in the eigenspace to the outputs. Implementations with the prediction of time series validate the concept.