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- W148323924 abstract "A new functional unit called fuzzy partitioning unit(FPU) is introduced. The FPU is a natural extension of a unit used in the conventional BP model to a multi-output version. An FPU with N outputs can partitions input space into N domains vaguely. A layered neural network with FPU's, which is called an FP model(FPM), is proposed and compared with the conventional BP model. As a measure of differences between desired outputs and real network's outputs, Kullback divergence (K.D.) that is the well known measure in information theory is also adopted and is compared with the mean squared error (M.S.K.) used in BP models. An learning algorithm designed so as to minimize K.D. rather than M.S.E. on FPM is shown to be excellent. That is, experiments of printed character recognition with layered neural networks show that the learning speed of FPM is much faster than BP models and the percent correct with FPM is also higher. Why an FPM with K.D. leads to an excellent leaning algorithm is also discussed." @default.
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- W148323924 date "1991-01-01" @default.
- W148323924 modified "2023-09-26" @default.
- W148323924 title "CHARACTER RECOGNITION BY A NEURAL NETWORK WITH FUZZY PARTITIONING UNITS" @default.
- W148323924 cites W1983701174 @default.
- W148323924 doi "https://doi.org/10.1016/b978-0-444-89178-5.50186-x" @default.
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