A dynamic T-S fuzzy model with a recurrent rule structure(TFM-RR)and its identification are proposed to improve the problem that conventional T-S fuzzy models can not exactly describe the time-varying characteristics of systems. A weighted feedback component that bases on the traditional T-S fuzzy model

is introduced in TFM-RR

and produces a new firing strength of the current rule from the weighted sum of the current firing strength and the previous firing strength. Thus

the firing strength of a rule varies dynamically and recursively

and effectively describes the dynamic process of the system. In order to make TFM-RR has fewer rules and good generalization capabilities

parameters of the antecedent of a rule are achieved using a fuzzy clustering algorithm that bases on the firing strength of the rule

while parameters of the consequent and the recursion are achieved by an integrated identification method that combines the support vector machine and a particle swarm optimization algorithm. Simulation results and comparisons with the hybrid clustering method on Box-Jenkins gas furnace show that the TFM-RR and its identification algorithm significantly reduce the mean variance by 1.2%

and show a better dynamic description ability.