Figure 2. Flowchart of the perceptron model.

(a) The training process flow chart. In this demonstration, a batch learning model is used to accelerate the converging speed. Here ‘n' represents the number of pattern, ranging from 1 to 9, ‘i' implies the index of a pixel of an input pattern and can be defined from 1 to 320, ‘j' is the number of output neuron that is 1–3. A correct classification during the inference phase means the active function value of a matching class of the input pattern is greater than other two classes. This network converges when all training patterns are correctly recognized. (b) The schematic of parallel read operation and how a pattern is mapped to the input. (c) The nine training images, which is a cropped and subsampled subset of the Yale Face Database¹⁹.