Characterization of t-norms on normal convex functions

TL;DR

This paper characterizes t-norms on the set of convex normal functions, solves key problems related to their convolution-based construction, and introduces computationally efficient t-norms to enhance type-2 fuzzy rule systems.

Contribution

It fully solves the problem of when convolution induces a t-norm on the set of convex normal functions and proposes easy-to-compute t-norms for practical applications.

Findings

Complete characterization of convolution-induced t-norms on convex normal functions

Identification of a class of computationally simple t-norms

Enhanced applicability of type-2 fuzzy rule systems through new t-norms

Abstract

Type-2 fuzzy set (T2 FS) were introduced by Zadeh in 1965, and the membership degrees of T2 FSs are type-1 fuzzy sets (T1 FSs). Owing to the fuzziness of membership degrees, T2 FSs can better model the uncertainty of real life, and thus, type-2 rule-based fuzzy systems (T2 RFSs) become hot research topics in recent decades. In T2 RFS, the compositional rule of inference is based on triangular norms (t-norms) defined on complete lattice $(L,\sqsubseteq)$ ( L is the set of all convex normal functions from [0,1] to [0,1], and , $\sqsubseteq$ is the so-called convolution order). Hence, the choice of t-norm on $(L,\sqsubseteq)$ may influence the performance of T2 RFS. Therefore, it is significant to broad the set of t-norms among which domain experts can choose most suitable one. To construct t-norms on $(L,\sqsubseteq)$, the mainstream method is convolution which is induced by two operators on the unit interval [0,1]. A key problem appears naturally, when convolution is a t-norm on $(L,\sqsubseteq)$. This paper has solved this problem completely. Moreover, note that the computational complexity of operators prevent the application of T2 RFSs. This paper also provides one kind of convolutions which are t-norms on $(L,\sqsubseteq)$ and extremely easy to calculate.