Int J Neural Syst - Evolving RBF neural networks for adaptive soft-sensor design.

Tópicos

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Resumo

This work presents an adaptive framework for building soft-sensors based on radial basis function (RBF) neural network models. The adaptive fuzzy means algorithm is utilized in order to evolve an RBF network, which approximates the unknown system based on input-output data from it. The methodology gradually builds the RBF network model, based on two separate levels of adaptation: On the first level, the structure of the hidden layer is modified by adding or deleting RBF centers, while on the second level, the synaptic weights are adjusted with the recursive least squares with exponential forgetting algorithm. The proposed approach is tested on two different systems, namely a simulated nonlinear DC Motor and a real industrial reactor. The results show that the produced soft-sensors can be successfully applied to model the two nonlinear systems. A comparison with two different adaptive modeling techniques, namely a dynamic evolving neural-fuzzy inference system (DENFIS) and neural networks trained with online backpropagation, highlights the advantages of the proposed methodology.

Resumo Limpo

work present adapt framework build softsensor base radial basi function rbf neural network model adapt fuzzi mean algorithm util order evolv rbf network approxim unknown system base inputoutput data methodolog gradual build rbf network model base two separ level adapt first level structur hidden layer modifi ad delet rbf center second level synapt weight adjust recurs least squar exponenti forget algorithm propos approach test two differ system name simul nonlinear dc motor real industri reactor result show produc softsensor can success appli model two nonlinear system comparison two differ adapt model techniqu name dynam evolv neuralfuzzi infer system denfi neural network train onlin backpropag highlight advantag propos methodolog

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