We describe the basics of fuzzy sets and fuzzy logic. Based upon the concept of linguistic values, which describe imprecise concepts using words, the basics of fuzzy rules and fuzzy inference are introduced. In the second part we briefly explain applications of fuzzy rules for function approximation using fuzzy graphs, clustering using fuzzy algorithms, and classification under uncertainty usin...

The single input connected fuzzy inference model (SIC model) by Hayashi et al. can decrease the number of fuzzy rules drastically in comparison with the conventional fuzzy inference models. In this paper, we first show the SIC model and its learning algorithm, and clarify the applicability of the SIC model by applying it to identification of nonlinear functions. c © 2013 The Authors. Published ...

This paper proposes a fuzzy abductive inference with degrees of manifestation. The fuzzy logic is applied to Peng and Reggia's abductive inference for handling the manifestation degrees. This method infers irredundant combinations of candidates with degrees of belief for the manifestations. Learning algorithm for updating the fuzzy causations and t-conorm parameter is also presented in this pap...

Fuzzy inference systems are studied from the point of view of systems of fuzzy relation equations. A fundamental interpolation condition is considered to be a crucial point of study in choosing proper inference method as well as a proper interpretation of a fuzzy rule base. The paper aims at additive interpretations and investigates their utilization from a theoretical point of view while their...

This paper introduces a complete framework of Modified Adaptive Fuzzy Inference Engine (MAFIE) and its application. The fuzzy with hybridization schemes has become of research interest in versatile applications over the past decade. The fuzzy hybridizations models are quite popular among practitioners or researchers in various advanced promising fields to help solve problems with a small number...

in statistical inference, the point estimation problem is very crucial and has a wide range of applications. when, we deal with some concepts such as random variables, the parameters of interest and estimates may be reported/observed as imprecise. therefore, the theory of fuzzy sets plays an important role in formulating such situations. in this paper, we rst recall the crisp uniformly minimum ...

Most information discovery processes need to understand the reasons of the success of the inference methods or the usability of the new information, which can sometimes be somehow explained with a few useful measures on the premises or on the chosen relations, logics and implications. This chapter presents a state of art on the latest concepts of measure, from the additive measures, to monotone...

This paper discusses the role of fuzzy databases in catalytic inference analysis. In knowledge discovery and inference control applications, it is important to represent common sense knowledge explicitly in the databases being analyzed. Such knowledge, typically expressed in fuzzy terms, can be introduced as catalytic relations. Analyzing the resulting augmented databases materializes new rules...