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A hardware implementation of the Takagi-Sugeno-Kan (TSK)-type recurrent fuzzy network (TRFN-H) for water bath temperature control is proposed in this paper. The TRFN-H is constructed by a series of recurrent fuzzy if-then rules built on-line through concurrent structure and parameter learning. To design TRFN-H for temperature control, the direct inverse control configuration is adopted, and owing to the structure of TRFN-H, no a priori knowledge of the plant order is required, which eases the design process. Due to the powerful learning ability of TRFN-H, a small network is generated, which significantly reduces the hardware implementation cost. After the network is designed, it is realized on a field-programmable gate array (FPGA) chip. Because both the rule and input variable numbers in TRFN-H are small, it is implemented by combinational circuits directly without using any memory. The good performance of the TRFN-H chip is verified from comparisons with computer-based proportional-integral fuzzy (PI) and neural network controllers for different sets of experiments on water bath temperature control.