In Mixed Fuzzy Rule Formation [Int. J. Approx. Reason. 32 (2003) 67] a method to extract mixed fuzzy rules from data was introduced. The underlying algorithm’s performance is influenced by the choice of fuzzy t-norm and t-conorm, and a heuristic to avoid conflicts between patterns and rules of different classes throughout training. In the following addendum to [Int. J. Approx. Reason. 32 (2003)...

Neuro-fuzzy classi cation systems make it possible to obtain a suitable fuzzy classi er by learning from data. Nevertheless, in some cases the derived rule base is hard to interpret. In this paper we discuss some approaches to improve the interpretability of neuro-fuzzy classi cation systems. We present modi ed learning strategies to derive fuzzy classi cation rules from data, and some methods ...

An approach is developed to MR brain images segmentation, based on pixel classification using Fuzzy Rule Based system and Fuzzy Similarity measures. The cerebral images are segmented into gray matter, white matter, and cerebrospinal fluid (CSF). Image preprocessing was first done to improve the quality of brain MR images and reducing artifacts. The feature vector was selected to be the pixel an...

Regarding the rapid growth of the size and complexity of simulation applications, designing applicable and affordable verification and validation (V&V) structures is an important problem. On the other hand, nowadays human behavior models are principles to make decision in many simulations and in order to have valid decisions based on a reliable human decision model, first the model must pass th...

The first published result in fuzzy rule interpolation was the α-cut based fuzzy rule interpolation, termed as KH fuzzy rule interpolation, originally devoted for complexity reduction. Some deficiencies of this method was presented later, such as subnormal conclusion for certain configuration of the involved fuzzy sets. However, since that several conceptually different fuzzy rule interpolation...

the concept of intelligently controlling the search process of gravitational search algorithm (gsa) is introduced to develop a novel data mining technique. the proposed method is called fuzzy gsa miner (fgsa-miner). at first a fuzzy controller is designed for adaptively controlling the gravitational coefficient and the number of effective objects, as two important parameters which play major ro...

In this paper, fuzzy economic order quantity (EOQ) model for inventory system with partial backorder is proposed. The fuzzy total relevance cost of the model is calculated under function principle. The optimal EOQ is derived using median rule. Fuzzy variables are appropriate when the exact information is unavailable. In the proposed model, the optimal solution for the fuzzy EOQ model is higher ...

This paper presents a framework for genetic fuzzy rule based classifier. First, a classification problem is divided into several two-class problems following a fuzzy class binarization scheme; next, a fuzzy rule is evolved for each two-class problem using a Michigan iterative learning approach; finally, the evolved fuzzy rules are integrated using the fuzzy class binarization scheme. In particu...

Fuzzy rule based classification systems are one of the most popular fuzzy modeling systems used in pattern classification problems. This paper investigates the effect of applying nine different T-norms in fuzzy rule based classification systems. In the recent researches, fuzzy versions of confidence and support merits from the field of data mining have been widely used for both rules selecting ...

Universal approximation is the basis of theoretical research and practical applications of fuzzy systems. Studies on the universal approximation capability of fuzzy systems have achieved great progress in recent years. In this paper, linear Takagi–Sugeno (T–S) fuzzy systems that use linear functions of input variables as rule consequent and their special case named simplified fuzzy systems that...