Abstract
In this paper, an intuitionistic fuzzy neural network model with a triangular membership and 1 minus triangular form non-membership functions is proposed. The network structure has six layers, and adopts Mandani’s fuzzy reasoning. A new fuzzy inference system is applied in the model, which contains hesitation margin as a part. The two steps dynamic optimal training algorithms for the IFNN is development, the first step is membership function parameters training, the second step is membership function parameters training, which can promise the summing of the trained membership and non-membership functions on finite universal set less then 1. An example is given to demonstrate the intuitionistic fuzzy neural network has a higher degree of accuracy and higher learning efficiency than fuzzy neural network.
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Zhou, X., Zhao, R., Zhang, L. (2013). An Intuitionistic Fuzzy Neural Network with Triangular Membership Function. In: Sun, Z., Deng, Z. (eds) Proceedings of 2013 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 254. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38524-7_90
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DOI: https://doi.org/10.1007/978-3-642-38524-7_90
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