Abstract
The paper presents a new type of fuzzy neural network, entitled Possibility Function based Neural Network (PFBNN). Its advantages consist in that it not only can perform as a standard neural network, but can also accept a group of possibility functions as input. The PFBNN discussed in this paper has novel structures, consisting in two stages: the first stage of the network is a fuzzy based and it has two parts: a Parameter Computing Network (PCN), followed by a Converting Layer (CL); the second stage of the network is a standard backpropagation based neural network (BPNN). The PCN in a possibility function based network can also be used to predict functions. The CL is used to convert the possibility function to a value. This layer is necessary for data classification. The network can still function as a classifier using only the PCN and the CL or only the CL. Using only the PCN one can perform a transformation from one group of possibility functions to another.
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Anastassiou, G.A., Iatan, I.F. (2016). A New Approach of a Possibility Function Based Neural Network. In: Anastassiou, G., Duman, O. (eds) Intelligent Mathematics II: Applied Mathematics and Approximation Theory. Advances in Intelligent Systems and Computing, vol 441. Springer, Cham. https://doi.org/10.1007/978-3-319-30322-2_10
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DOI: https://doi.org/10.1007/978-3-319-30322-2_10
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