Recursive least-squares method with membership functions

Linear regression with the recursive least-squares algorithm is widely used for parameter estimation. However, in a number of cases, linear regression is not sufficient to accurately correlate a process. A method combining the recursive least-squares algorithm and membership functions is presented to overcome the shortcoming of traditional linear regression in that multiple linear sub-models with different coefficients are used. The model coefficients can be tuned recursively according to the membership functions preset for the models. Simulation results show that this method can be used with good performance for nonlinear process, which is previously difficult to be estimated by using the traditional linear regression.