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• W1866544748 abstract "In this paper, we propose a complex-valued Takagi-Sugeno-Kang type-0 neuro-fuzzy inference system (CNFIS) and develop for it, a gradient-descent based learning algorithm to solve classification problems. The gradient-descent based learning algorithm is derived based on Wirtinger calculus: which preserves the amplitude-phase correlation. The performance of the developed algorithm is evaluated on a set of four binary classification problems and three multi-category classification problems. Comparison with various real-valued and complex-valued classifiers show the improved performance of CNFIS." @default.
• W1866544748 created "2016-06-24" @default.
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• W1866544748 date "2012-01-01" @default.
• W1866544748 modified "2023-09-22" @default.
• W1866544748 title "Complex-Valued Neuro-Fuzzy Inference System Based Classifier" @default.
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• W1866544748 doi "https://doi.org/10.1007/978-3-642-35380-2_41" @default.
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