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Author(s): Khan, E.R. |
Year: 2004
Citation: Patent number: 5058180 Issue date: Oct 15, 1991
Abstract: A self-organizing neural network having input and output neurons mutually coupled via bottom-up and top-down adaptive weight matrics performs pattern recognition while using substantially fewer neurons and being substantially immune from pattern distortion or rotation. The network is first trained in accordance with the adaptive resonance theory by inputting reference pattern data into the input neurons for clustering within the output neurons. The input neurons then receive subject pattern data which are transferred via a bottom-up adaptive weight matrix to a set of output neurons. Vigilance testing is performed and multiple computed vigilance parameters are generated. A predetermined, but selectively variable, reference vigilance parameter is compared individually against each computed vigilance parameter and adjusted with each comparison until each computed vigilance parameter equals or exceeds the adjusted reference vigilance parameter, thereby producing an adjusted reference...
Topics:
Machine Learning,
