Abstract
A new strategy to realize an FPGA implementation of a soft sensor for an industrial process is proposed. In order to cope with the problem of small data sets in the identification of a non linear model the proposed approach is based on the integration of bootstrap re-sampling, noise injection and stacked neural networks (NNs), using the Principal Component Analysis (PCA). The aggregated final NN-PCA system has been implemented on Field Programmable Gate Array (FPGA). The proposed method has been applied to develop a soft sensor for the estimation of the freezing point of kerosene in an atmospheric distillation unit (topping) working in a refinery in Sicily, Italy.
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