Fuzzy set representation of neural network classification boundaries

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Abstract
In neural network classification techniques, the uncertainty of a new observation belonging to a particular class is difficult to express in statistical terms. On the other hand, statistical classification techniques are also poor for supplying uncertainty information for new observations. However, the use of fuzzy sets is a promising approach to providing imprecise class membership information. The monotonic function neural network is a tool that can be used to develop fuzzy membership functions. This research suggests that a multiarchitecture monotonic function neural network can be used for fuzzy set representation of classification boundaries in monotonic pattern recognition.

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