in the present paper, we introduce and study a fuzzy vector equilibrium problem and prove some existence results with and without convexity assumptions by using some particular forms of results of textit{kim} and textit{lee} [w.k. kim and k.h. lee, generalized fuzzy games and fuzzy equilibria, fuzzy sets and systems, 122 (2001), 293-301] and textit{tarafdar} [e. tarafdar, fixed point theorems i...

Constitutive modeling of clay is an important research in geotechnical engineering. It is difficult to use precise mathematical expressions to approximate stress-strain relationship of clay. Artificial neural network (ANN) and support vector machine (SVM) have been successfully used in constitutive modeling of clay. However, generalization ability of ANN has some limitations, and application of...

according to recent developments of predicting methodsin financial markets, and since the stock price is one of the mostimportant factors for investment decision-making, and its predictioncan play an important role in this field, the aim of this study is toprovide a model to predict the stock price movement with highaccuracy. accordingly, a hybrid model for predicting the stock pricemovement us...

Network intrusion detection is of great importance in the research field of information security in computer networks. In this paper, we concentrate on how to automatically detect the network intrusion behavior utilizing fuzzy support vector machine. After analyzing the related works of the proposed paper, we introduce the main characterics of fuzzy support vector machine, and demonstrate its f...

The design of a neuro-fuzzy system based on a radial basis function (RBF) network architecture and using support vector learning is considered. Typically, a neuro-fuzzy model structure is created from numerical data, however the common modeling techniques may introduce unnecessary redundancy into the rule base. It is of great interest to reduce the number of fuzzy rules. The proposed method pro...

In this paper we reason about the usefulness of two recent trends in fuzzy methods in machine learning. That is, we discuss both fuzzy support vector machines (FSVMs) and the extraction of fuzzy rules from SVMs. First, we show that an FSVM is identical to a special type of SVM. Second, we categorize and analyze existing approaches to obtain fuzzy rules from SVMs. Finally, we question both trend...