Granularrules have been extensively used for classification in fuzzy datasets to promote the advancement of artificial intelligence. However, due diversity data types, how improve readability extracted granular rules while ensuring efficiency is always a challenge. Since reduct computing (GrC) can simplify real complex problem and dataset, this article carries out rule learning from perspective...

Fuzzy Newton-Cotes method for integration of fuzzy functions that was proposed by Ahmady in [1]. In this paper we construct error estimate of fuzzy Newton-Cotes method such as fuzzy Trapezoidal rule and fuzzy Simpson rule by using Taylor's series. The corresponding error terms are proven by two theorems. We prove that the fuzzy Trapezoidal rule is accurate for fuzzy polynomial of degree one and...

land cover information is one of the most important prerequisite in urban management system. in this way remote sensing, as the most economic technology, is mainly used to produce land cover maps. considering the complicated and dense urban areas in third world countries, object based approaches are suggested as an effective image processing technique. the purpose of this paper are the introduc...

Fuzzy modeling of high-dimensional systems is a challenging topic. This study proposes an effective approach to data-based fuzzy modeling of high-dimensional systems. The proposed method works on the fuzzification layer and tries to use two-dimensional membership functions instead of onedimensional ones. This approach reduces fuzzy rule base radically due to using of two-dimensional membership ...

When designing any type of fuzzy rule based system, considerable effort is placed in identifying the correct number of fuzzy sets and the fine tuning of the corresponding membership functions. Once a rule base has been formulated a fuzzy inference strategy must be applied in order to combine grades of membership. Considerable time and effort is spent trying to determine the number of fuzzy sets...

Recently, tuning the weights of the rules in Fuzzy Rule-Base Classification Systems is researched in order to improve the accuracy of classification. In this paper, a margin-based optimization model, inspired by Support Vector Machine classifiers, is proposed to compute these fuzzy rule weights. This approach not only  considers both accuracy and generalization criteria in a single objective fu...

In this paper we present the application of a particular neuro-fuzzy system, named KERNEL, to the problem of differential diagnosis of erythematosquamous diseases, which represents a major problem in dermatology. A multistep learning strategy is adopted to obtain, starting directly from available data, a fuzzy rule base that can be used to identify the particular disease. The obtained classific...

Dynamic changes of object positions provide an important clue for video characterization. In the present work, we exploit the dynamic information present over different frames of a sports video to characterize the change in the configuration of players across different frames. For scene dynamic characterization firstly location of players are detected by using motion based segmentation. We then...

Fuzzy inference process usually involves the use of fuzzy rule base consisting in several fuzzy rules. Overall output can be obtained by aggregation of outputs of all rules. To obtain an output of individual rule the relevancy of this rule is calculated. Then the individual output is obtained from the relevancy and the consequent of the rule. Such process can be realised using an operator that ...