Department of Mathematics, University of Coimbra, Portugal Reference databases for the accuracy assessment of land cover maps are assumed to represent the true land cover in a set of selected locations. Although, the elaboration of reference databases through field visits or photo interpretation of aerial images is a hard and difficult task due to uncertainty in the assignment of the most corre...

System modeling with fuzzy rule-based systems (FRBSs), i.e. fuzzy modeling (FM), usually comes with two contradictory requirements in the obtained model: the interpretability, capability to express the behavior of the real system in an understandable way, and the accuracy, capability to faithfully represent the real system. While linguistic FM (mainly developed by linguistic FRBSs) is focused o...

One of the most important aspects of fuzzy systems is that they are easily understandable and interpretable. This property, however, does not come for free but poses some essential constraints on the parameters of a fuzzy system (like the linguistic terms), which are sometimes overlooked when learning fuzzy system automatically from data. In this paper, an objective function-based approach to l...

-The traditional query in relational database is unable to satisfy the needs for dealing with fuzzy linguistic values. In this paper, a new data query technique composed of fuzzy theory and MS-SQL is provided. Here, the query can be implemented for fuzzy linguistic variables query via an interface to Microsoft ASP.NET. It is being applied to an realistic instance i.e. questions could be express...

In this paper, we present an evolutionary multiobjective learning model achieving positive synergy between the Inference System and the Rule Base in order to obtain simpler, more compact and still accurate linguistic fuzzy models by learning fuzzy inference operators together with Rule Base. The Multiobjective Evolutionary Algorithm proposed generates a set of Fuzzy Rule Based Systems with diff...

Fuzzy systems can be used for function approximation based on a set of linguistic rules. We present a method to obtain the necessary parameters for such a fuzzy system by a neuro-fuzzy training method. The learning algorithm is able to determine the structure and the parameters of a fuzzy system from sample data. The approach is an extension to our already published NE-FCON and NEFCLASS models ...

Supplier selection is a multi-Criteria problem. This study proposes a hybrid model for supporting the suppliers’ selection and ranking. This research is a two-stage model designed to fully rank the suppliers where each supplier has multiple Inputs and Outputs. First, the supplier evaluation problem is formulated by Data Envelopment Analysis (DEA), since the regarded decision deals with uncertai...

In this paper, a multi-objective constrained optimization model is proposed to improve interpretability of TSK fuzzy models. This approach allows a linguistic approximation of the fuzzy models. Three different multi-objective evolutionary algorithms (MONEA, ENORA and NSGA-II) are used together with neural network techniques. These algorithms are checked out in the approximation of a dynamic non...

This paper presents an evolutionary Multiobjective learning model achieving positive synergy between the Inference System and the Rule Base in order to obtain simpler and still accurate linguistic fuzzy models by learning fuzzy inference operators and applying rule selection. The Fuzzy Rule Based Systems obtained in this way, have a better trade-off between interpretability and accuracy in ling...