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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...
This paper proposes a new fuzzy regression model, i.e., the fuzzy system constructed by rule generation and iterative linear support vector regression (FS-RGLSVR) for structural risk minimization. The FS-RGLSVR is composed of Takagi–Sugeno (TS)-type fuzzy if–then rules. These rules are automatically constructed by a self-splitting rule generation algorithm that introduces the self-splitting technique...
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...
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