Abstract
Many studies on learning of fuzzy inference systems have been made. Specifically, it is known that learning methods using VQ (Vector Quantization) and SDM (Steepest Descend Method) are superior to other methods. We already proposed new learning methods iterating VQ and SDM. In their learning methods, VQ is used only in determination of parameters for the antecedent part of fuzzy rules. In order to improve them, we added the method determining of parameters for the consequent part of fuzzy rules to processing of VQ and SDM. That is, we proposed a learning method composed of three stages as VQ, GIM(Generalized Inverse Matrix) and SDM in the previous paper. In this paper, the ability of the proposed method is compared with other ones using VQ. As a result, it is shown that the proposed method outperforms conventional ones using VQ in terms of accuracy and the number of rules.
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Miyajima, H., Shigei, N., Miyajima, H. (2016). The Ability of Learning Algorithms for Fuzzy Inference Systems Using Vector Quantization. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9950. Springer, Cham. https://doi.org/10.1007/978-3-319-46681-1_57
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DOI: https://doi.org/10.1007/978-3-319-46681-1_57
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