Title :
Hetero associative neural network for pattern recognition
Author :
Lu, Taiwei ; Xu, Xin ; Wu, Shudong ; Yu, Francis T S
Author_Institution :
Dept. of Electr. Eng., Pennsylvania State Univ., University Park, PA, USA
Abstract :
An inter-pattern association (IPA) neural network model is presented in which basic logical operations are used to determine the association among common and special features of reference patterns. Hetero- and auto-associative memory are synthesized by applying a generalized logical rule. Computer simulations for pattern recognition by using the IPA model have shown a better performance and a higher storage capacity than the Hopfield model. A 2-D adaptive optical neural network is used to perform parallel neurocomputations. Since the interconnection weight matrix for the IPA model has a tristate structure, the dynamic range imposed on a spatial light modulator is rather relaxed, and the interconnections are much simpler than for the Hopfield model
Keywords :
content-addressable storage; neural nets; pattern recognition; 2-D adaptive optical neural network; hetero associative neural network; parallel neurocomputations; pattern recognition; reference patterns; storage capacity; Adaptive optics; Adaptive systems; Computer simulation; Network synthesis; Neural networks; Optical computing; Optical fiber networks; Optical interconnections; Optical modulation; Pattern recognition;
Conference_Titel :
Systems, Man and Cybernetics, 1989. Conference Proceedings., IEEE International Conference on
Conference_Location :
Cambridge, MA
DOI :
10.1109/ICSMC.1989.71378
