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A central problem of fuzzy modelling is the generation of fuzzy rules that fit the data to the highest possible extent. In this study, we present a method for automatic generation of fuzzy rules from data. The main advantage of the proposed method is its ability to perform data clustering without the requirement of predefining any parameters including number of clusters. The proposed method creates...
Fuzzy classifiers (FCs) are based on fuzzy if-then classification rules. Traditional FCs use either zero- or first-order Takagi-Sugeno (TS)-type fuzzy rules, where the consequent of a fuzzy rule is a linear decision function and may restrict the rule discrimination capability. This paper uses a high-order FC (HOFC) that expands the entire rule-mapped consequent space of a first-order TS-type fuzzy...
In this paper, a novel learning method based on kernelized fuzzy clustering and least squares support vector machines (LSSVM) is presented to improve the generalization ability of a Takagi-Sugeno-Kang (TSK) fuzzy modeling. Firstly, the fuzzy partition of the product space of input and output is obtained by kernelized fuzzy clustering. Then, a computationally efficient numerical method is proposed...
Intrusion detection has become the important component of the network security. Many intelligent intrusion detection models are proposed, but the performance and efficiency are not satisfied to real computer network system. This paper extends these works by applying a new high efficient technique, named twin support vector machines (TWSVM), to intrusion detection. Using the KDD'99 data set collected...
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