Singleton-Based Two-String Inference in Recurrent Fuzzy Systems

Two-string fuzzy inference consists of two separate inference mechanisms: One conventional fuzzy inference system that processes recommending rules, as well as a mechanism for processing negative rules, which prevent the system from outputting their associated values when their premise is fulfilled. Two-string inference has valuable applications in pattern recognition and control tasks. We present a method rendering two-string inference applicable and computationally feasible in recurrent fuzzy systems, i.e. Mamdami-type fuzzy inference systems equipped with defuzzified state feedback. We show the efficiency of our approach by means of an illustrative example from biological systems modelling and suggest application areas for recurrent two-string fuzzy systems.