Input Vector Identification and System Model Construction by Average Mutual Information
Paul B. Deignan, Peter H. Meckl, Matthew Albert Franchek, Salim A. Jaliwala, George Zhu · 2000
Abstract A methodology for the intelligent, model-independent selection of an appropriate set of input signals for the system identification of an unknown process is demonstrated. In modeling this process, it is shown that the terms of a simple nonlinear polynomial model may also be determined through the analysis of the average mutual information between inputs and the output. Average mutual information can be thought of as a nonlinear correlation coefficient and can be calculated from input/output data alone. The methodology described here is especially applicable to the development of virtual sensors.