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The complex nonlinear systems, which are difficult to be mathematically modelled, can be described by a fuzzy model. This article attempts to improve and to address the problems concerning the systematic fuzzy-logic modelling of multi-input-muiti-output (MIMO) systems, by introducing the following three concepts. 1) A generalized and parameterized reasoning mechanism constructed based on the weighted sum of the normalized defuzzified output value of each individual rule. Then the crisp outputs of the fuzzy model can be directly calculated from the crisp inputs using the parameterized reasoning mechanism. This reasoning mechanism is suitable for online learning and real-time control applications. 2) A gradient-descent based parameter adjustment to tune the parameters of reasoning mechanism (which are equal to the number of rules) instead of the existing heuristic complex parameter identification in the literature. 3) An improved method to select the main system input from all input candidates in the presence of singularity. The proposed systematic method of fuzzy modelling has the advantages of simplicity, flexibility, and high accuracy. The two example data, which have been widely used in the literature as benchmark, are used to evaluate the performance of the proposed method.

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