In complex multidimensional problems with a highly nonlinear input-output relation, inconsistent or redundant rules can be found in the fuzzy model rule base, which can result in a loss of accuracy and interpretability. Moreover, the rules could not cooperate in the best possible way. It is known that the use of rule weights as a local tuning of linguistic rules, enables the linguistic fuzzy mo...

This paper discusses fuzzy reasoning for approximately realizing nonlinear functions by a small number of fuzzy if-then rules with different specificity levels. Our fuzzy rule base is a mixture of general and specific rules, which overlap with each other in the input space. General rules work as default rules in our fuzzy rule base. First we briefly describe existing approaches to the handling ...

In this contribution we carry out an analysis of the rule weights and Fuzzy Reasoning Methods for Fuzzy Rule Based Classification Systems in the framework of imbalanced data-sets with a high imbalance degree. We analyze the behaviour of the Fuzzy Rule Based Classification Systems searching for the best configuration of rule weight and Fuzzy Reasoning Method also studying the cooperation of some...

This paper proposes two kinds of fuzzy abductive inference in the framework of fuzzy rule base. The abductive inference processes described here depend on the semantic of the rule. We distinguish two classes of interpretation of a fuzzy rule, certainty generation rules and possible generation rules. In this paper we present the architecture of abductive inference in the first class of interpret...

The present paper is a humble attempt to develop a fuzzy function approximator which can completely self-generate its fuzzy rule base and input-output membership functions from an input-output data set. The fuzzy system can be further adapted to modify its rule base and output membership functions to provide satisfactory performance. This proposed scheme, called generalised influential rule sea...

Within the field of linguistic fuzzy modeling with fuzzy rule-based systems, the automatic derivation of the knowledge base from numerical data is an important task. In this contribution, we propose a new approach to automatically learn the whole knowledge base, combining two different strategies for rules derivation and fuzzy partitions definition, working cooperatively in order to obtain accu...

The sparse fuzzy model identification (SFMI) toolbox is a Matlab based software that was developed to facilitate the creation of fuzzy systems with a compact and low complexity rule base. The objective of this paper to report some extensions and new features of the toolbox that aim the widening of the pool of the applicable approaches and methods in the process of automatic rule base creation f...