Multifunctional layered network with quantizer neurons

The authors propose a multifunctional layered network (MFLN) with a quantizer neuron model and describe the principles of the quantizer neuron and the structure of the network for a character recognition system. Each layer of the MFLN has a specific function defined by the quantizer input of the quantizer neuron, and its learning speed is extremely fast. The authors have applied it to a character recognition system and tested its initial and supplemental learning performance in comparison with three other network models (RCE networks, LVQ3, and a multilayered neural network with back-propagation). For initial learning of ten fonts, the MFLN is fastest, and it is 40 times faster than the multilayered neural network with back-propagation. For supplemental learning with seven further fonts also, the MFLN is the fastest, and it is 600 times faster than the multilayered neural network with back-propagation. The recognition rate for 10 of the fonts learned initially is 97.4% after the MFLN has learned supplementary fonts, and the MFLN displays the lowest degradation of the recognition rate of initially learned fonts