A comparative study with feedforward pdp models for alphanumeric character recognition
Yoshinaga Kato, Yasuo Tan, Toshiaki Ejima · Systems and Computers in Japan · 1991
Abstract This paper discusses the alphanumeric character recognition using two kinds of PDF (parallel distributed processing) models. The models considered are the FPM (fuzzy partition model) of a new type with multiple input/output units, and the conventional BP (back‐propagation) model. Two BP algorithms with different error evaluation functions are applied to those models, and the number of trainings and the recognition rate in the multifont recognition are compared. As a result of experiment, it is shown that FPM with Kullback divergence evaluation can realize a more accurate recognition with fever number of trainings, compared to the conventional BP model. The reason for the reduction of the number of trainings is discussed when the mutual inhibition in FPM unit as well as the Kullback divergence as the error evaluation function are employed.