A TSK-type recurrent fuzzy network for dynamic systems processing via supervised and reinforcement learning

Abstract

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_--