Identification of hinging hyperplane autoregressive exogenous model using efficient mixed-integer programming

A computationally efficient algorithm for hinging hyperplane autoregressive exogenous (HHARX) model identification via mixed-integer programming technique is proposed in this paper. The HHARX model is attractive since it accurately approximates a general nonlinear process as a sum of hinge functions and preserves the continuity even in a piecewise affine form. Traditional mixed-integer programming-based method for HHARX model identification can only be applied on small-scale input/output datasets due to its significant computational demands. The contribution of this paper is to develop a sequential optimization approach to build accurate HHARX model more efficiently on a relatively large number of experimental data. Moreover, the proposed framework can handle more difficult and practical cases in piecewise model identification, such as: limited submodel switching, missing output data and specified steady state. Finally, the efficiency and accuracy of the proposed computational scheme are demonstrated through modeling of two simulated examples and a pilot-scale heat exchanger.