The poor scaling behavior of grid-partitioning fuzzy systems in case of increasing data dimensionality suggests using fuzzy systems with a scatter-partition of the input space. Jang has shown that zero-order Sugeno fuzzy systems are equivalent to radial basis function networks (RBFNs). Methods for nding scatter partitions for RBFNs are available, and it is possible to use them for creating scat...

Fuzzy systems address the imprecision of the input and output variables, which formally describe notions like “rather warm” or “pretty cold”, while provide a behaviour that depends on fuzzy data. This class of systems are classically represented by means of Fuzzy Inference Systems (FIS), a computing framework based on the concepts of fuzzy if-then rules and fuzzy reasoning. Even if FIS are larg...

Over the last years, a number of methods have been proposed to automatically learn and optimize fuzzy rule bases from data. The obtained rule bases are usually robust and allow an interpretation even for data sets that contains imprecise or uncertain information. However, most of the proposed methods are still restricted to learn and/or optimize single layer feed-forward rule bases. The main di...

Potato image segmentation is an important part of image-based potato defect detection. This paper presents a robust potato color image segmentation through a combination of a fuzzy rule based system, an image thresholding based on Genetic Algorithm (GA) optimization and morphological operators. The proposed potato color image segmentation is robust against variation of background, distance and ...

This paper presents an algorithm to extract rules relating input/output data and including prior knowledge. The rules are created in the environment of fuzzy systems. The fuzzy sets describing the system are constructed within a framework of linguistic integrity to guarantee its interpretability in the linguistic context. Two algorithms are presented in this paper. The main algorithm is the aut...

Performance evaluation in conventional data envelopment analysis (DEA) requires crisp numerical values. However, the observed values of the input and output data in real-world problems are often imprecise or vague. These imprecise and vague data can be represented by linguistic terms characterised by fuzzy numbers in DEA to reflect the decision-makers' intuition and subjective judgements. This ...

Existence of outlier data among the observation data leads to inaccurate results in modeling. Detection to omit or lessen the impact of such data has a significant effect to make corrections in a model. Either elimination or reduction of the outlier data influence is two ways to prevent their negative effect on the modeling. Both approaches of elimination and impact reduction are taken into acc...

different industrial decision makers and managers in our country, try to select and organize locations for aggregating industrial units, estates and areas with respect to land use planning visions and industrial development strategies. in this regard, considering large quantity of the input data and diverse criteria affecting this application, it is complicated and difficult to optimally make d...

a new approach for pole placement of nonlinear systems using state feedback and fuzzy system is proposed. we use a new online fuzzy training method to identify and to obtain a fuzzy model for the unknown nonlinear system using only the system input and output. then, we linearized this identified model at each sampling time to have an approximate linear time varying system. in order to stabilize...