An optimally robust detection of an input pattern from standard patterns
Chia-Lun John Hu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2004
When a binary pattern such as the edge-detected object, or the contour of a group of features, etc., is selected from a first layer (a preprocessing layer) of a neural network system according to the designer's choice, the refined and accurate recognition of this object is subject to the accurate but optimally robust comparison of this input pattern to a limited number of standard patterns. Optimum robustness here means that each standard pattern has an allowed variable range which is determined automatically in the noniterative learning, and that the chance for an unknown pattern to access each range is equal. This paper will report the derivation and the analysis of the neural network system from the point of view of discrete algebra and matched filters. Its design principle relates closely to that of the universal mapping in a noniterative neural system and that of the matched filter in an electronic communication system.