A tensor product (TP) model has an infinite number of alternatives. These variants can be readily derived by the TP model transformation that can variate the number of fuzzy rules, the number of antecedent and consequent sets and, further, the shape of the antecedent fuzzy sets. The related literature has quite deep analysis that modifying these features of a TP model has a crucial role in further design. The latest variants of the TP model transformation, emerged about a year ago, are capable of variating the input space of a TP model. The goal of this paper is to analyse this recently emerged new feature through the example of the gantry crane state space model.