-Insensitive Criterion and Structure Risk Based Radius-basis-function Neural-network Modeling

An - insensitive criterion and structure risk based Radius-Basis-Function Neural-Network (RBF-NN) modeling method is proposed. By -introducing insensitive criterion and the item of structure risk, the RBF-NN learning is transformed into the linear regression and Quadratic Program (QP) optimization issue. Compared with the traditional least-square-criterion based RBF-NN training algorithms, the proposed method is much more robust to noise data and small size of datasets. Through the simulation experiments on the synthetic and real-word datasets, the above virtues are confirmed.