A new transformed input-domain ANFIS for highly nonlinear system modeling and prediction

In two or more-dimensional systems where the components of the sample data are strongly correlated, it is not proper to divide the input space into several subspaces without considering the correlation. In this paper, we propose the usage of the method of principal component in order to uncorrelate and remove any redundancy from the input space or the adaptive-neuro fuzzy inference systems (ANFIS). This leads to an effective partition of the input space to the fuzzy model and significantly reduces the modeling error. A computer simulation for three frequently used benchmark problems shows that ANFIS with the uncorrelation process performs better than the original ANFIS.