This paper describes an approach to classification of noisy signals using a technique based on the fuzzy ARTMAP neural network (FAMNN). The proposed method is a modification of the testing phase of the fuzzy ARTMAP that exhibits superior generalization performance compared to the generalization performance of the standard fuzzy ARTMAP in the presence of noise. An application to textured gray-sc...

A brief summary on and comprehensive understanding of fuzzy optimization is presented. This summary is made on aspects of fuzzy modelling and fuzzy optimization, classification and formulation for the fuzzy optimization problems, models and methods. The importance of interpretation of the problem and formulation of the optimal solution in fuzzy sense are emphasized in the summary of the fuzzy o...

In this paper, we introduce the notion θ -Euclidean k–fuzzy ideal in semirings and to study the properties of the image and pre image of a θ -Euclidean k–fuzzy ideal in a semirings under epimorphism. Keywords—semiring, fuzzy ideal, k–fuzzy ideal,θ -Euclidean Lfuzzy ideal, θ -Euclidean fuzzy k–ideal, θ -Euclidean k-fuzzy ideal. 2000 AMS Classification: 16Y60, 13E05, 03G25.

Sensors that measure yield, temperature, electrical conductivity of milk, and animal activity can be used for automated cow status monitoring. The occurrence of false-positive alerts, generated by a detection model, creates problems in practice. We used fuzzy logic to classify mastitis and estrus alerts; our objective was to reduce the number of false-positive alerts and not to change the level...

This paper describes a general fuzzy min-max (GFMM) neural network which is a generalization and extension of the fuzzy min-max clustering and classification algorithms developed by Simpson. The GFMM method combines the supervised and unsupervised learning within a single training algorithm. The fusion of clustering and classification resulted in an algorithm that can be used as pure clustering...

This contribution proposes a technique for Fuzzy Rule Based Classification Systems (FRBCSs) based on a multi-classifier approach using fuzzy preference relations for dealing with multi-class classification. The idea is to decompose the original data-set into binary classification problems using a pairwise coupling approach (confronting all pair of classes), and to obtain a fuzzy system for each...

This paper presents a fuzzy set theory based approach to Chinese sentence-level sentiment classification. Compared with traditional topic-based text classification techniques, the fuzzy set theory provides a straightforward way to model the intrinsic fuzziness between sentiment polarity classes. To approach fuzzy sentiment classification, we first propose a fine-to-coarse strategy to estimate s...

In an earlier work, Lee et al. [1] presented a simple and fast fuzzy classifier that employed fuzzy entropy to evaluate pattern distribution information in a pattern space. In this paper, we extend his work to propose a new fuzzy classifier based on hierarchical fuzzy entropy (FC-HFE). We retained the main parts of the original structure and modified some methods (e.g., decision of the number o...

This paper proposes an adaptive method to construct a fuzzy rule-based classification system with high performance for pattern classification problems. The proposed method consists of two procedures: an error correction-based learning procedure and an additional learning procedure. The error correction-based learning procedure adjusts the grade of certainty of each fuzzy rule by its classificat...

For complex and high-dimensional problems, data-driven identification of classifiers has to deal with structural issues like the selection of the relevant features and effective initial partition of the input domain. Therefore, the identification of fuzzy classifiers is a challenging topic. Decision-tree (DT) generation algorithms are effective in feature selection and extraction of crisp class...