A novel signal validation method applied to a stochastic process

A novel methodology is presented for the purposes of modelling the process dynamics of neutron flux during induced boiling of the coolant in a nuclear reactor environment. The proposed approach is based on the utilisation of radial basis functions from the point of view of function approximation, regularisation, and noisy interpolation. Data-driven multiple-input/multiple-output radial basis function networks are subjected to faulty input signals and their signal validation capabilities are studied. Networks hased on radial basis functions employ training procedures that are substantially faster than the methods used in training multi-layer perception networks. Auto-associative schemes of the proposed technique are shown capable of identifying faulty process signal measurements and provide the operator with an estimate of the actual process value. The data utilised for the purposes of this project are obtained from an experimental facility constructed to simulate boiling phenomena and positioned next to the core of the IRI research reactor