Adaptive segmentation of quantizer neuron architecture (ASQA)

The authors have previously proposed a multi functional layered network (MFLN) employing a quantizer neuron model and proved that a learning speed of MFLN is the fastest among RCE networks, LVQ3 and multi-layered neural network with backpropagation. The authors also proved that MFLN has very nice supplemental learning performance and can realize adaptive learning or filtering. One of the biggest issues of neural networks is how to design the network structure. In this paper the authors propose an adaptive segmentation of quantizer neuron architecture (ASQA) for answering the above issue and apply them to handwritten character recognition. The networks based on ASQA consist of quantizer neurons which can proliferate themselves and form the optimum network structure for the recognition automatically during training. As a result, there is no need to design the structure of the networks and the average accuracy of the closed and the open test of 27,200 handwritten numeric characters increased to 99.6%. The best tuning of a segmentation threshold of quantizer neurons produced the optimum network size of ASQA.