Abstract
A fuzzy hyperline segment clustering neural network (FHLSCNN) and its learning algorithm is proposed. This algorithm can learn ill-defined nonlinear cluster boundaries in a few passes and is suitable for on-line adaptation. The FHLSCNN is superior compared to the fuzzy min-max clustering neural network (FMN) proposed by Simpson.
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