<title>Feature competition and feature extraction in a novel neural network pattern recognition system</title>

Abstract

As we published in the last few years, when the given input- output training vector pairs satisfy a PLI (positive-linear- independency) condition, the training of a hard-limited neural network to recognize untrained patterns can be achieved noniteratively with very short training time and very robust recognition. The key feature in this novel pattern recognition system is the use of slack constants in solving the connection matrix when the PLI condition is satisfied. Generally there are infinitely many ways of selecting the slack constants for meeting the training- recognition goal, but there is only one way to select them if an optimal robustness is sought in the recognition of the untrained patterns. This particular way of selecting the slack constants carries some special physical properties of the system--the automatic feature extraction in the learning mode and the automatic feature competition in the recognition mode. Physical significance as well as mathematical analysis of these novel properties are to be explained in detail in this paper. Real-time experiments are to be presented in an unedited movie. It is seen that in the system, the training of 4 hand-written characters is close to real time (< 0.1 sec.) and the recognition of the untrained hand-written characters is > 90% accurate.