Self-organising map for large scale processes monitoring

A feed-forward neural network is proposed for monitoring operating modes of large scale processes. A Gaussian hidden layer associated with a Kohonen output layer map the principal features of measurements of state variables. Subsets of selective neurons are generated into the hidden layer by means of self adapting of centers and dispersions parameters of the Gaussian functions. The output layer operates like a data fusion operator by means of adapting the hidden-to-output matrix of weights through a winner takes all strategy. The algorithm is tested with the Tennessee Eastman Challenge Process. The results prove that the proposed neural network clearly maps the different operating modes.