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In this paper, a TSK-type Recurrent Fuzzy Network (TRFN) structure is proposed. The proposal calls for a design of TRFN under either supervised or reinforcement learning. Set forth first is a recurrent fuzzy network which develops from a series of recurrent fuzzy if-then rules with TSK-type consequent parts. TRFN design under the two learning environments, supervised and rcinforcement, is next advanced. For TRFN with Supervised learning (TRFN-s), an on-line learning algorithm with concurrent structure and parameter learning is proposed. For reinforcement learning, TRFN with Genetic learning (TRFN-G) is put forward.To demonstrate the superior properties of TRFN, TRFN-S is applied to dynamic system identification and TRFN-G to dynamic system control, and the efficiency of TRFN is verified_--

A Takagi-Sugeno (TS)-type Fuzzy System trained by Support Vector Machine (TSFS-SVM) is proposed and applied to skin color segmentation. In TSFS-SVM, the consequence of each rule is of TS-type. Instead of being trained by neural learning, TSFS-SVM is trained by SVM with the objective to obtain a higher generalization ability. Performance of TSFS-SVM is verified through skin color segmentation problem. To represent color information by histogram as accurately as possible, non-uniform partition of HS space is used. Histogram information from images under different environments is used to train TSFS-SVM. Advantage of TSFS-SVM is verified by comparisons with other compared methods.

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