Separate learning system and method using two-layered neural network having target values for hidden nodes
Patent No. 7,734,555
Issued: June 8, 2010
Inventors: Lee; Ju Hong (Incheon, KR), Choi; Bum Ghi (Seoul, KR), Park; Tae
Su (Incheon, KR)
Disclosed herein is a separate learning system and method using a two-layered neural network having target values for hidden nodes. The separate learning system of the present invention includes an input layer for receiving training data from a user, and including at least one input node. A hidden layer includes at least one hidden node. A first connection weight unit connects the input layer to the hidden layer, and changes a weight between the input node and the hidden node. An output layer outputs training data that has been completely learned. The second connection weight unit connects the hidden layer to the output layer, changing a weight between the output and the hidden node, and calculates a target value for the hidden node, based on a current error for the output node. A control unit stops learning, fixes the second connection weight unit, turns a learning direction to the first connection weight unit, and causes learning to be repeatedly performed between the input node and the hidden node if a learning speed decreases or a cost function increases due to local minima or plateaus when the first connection weight unit is fixed and learning is performed using only the second connection weight unit, thus allowing learning to be repeatedly performed until learning converges to the target value for the hidden node.
Reference to Related Applications
This application claims an invention which was disclosed in Korean (Republic of) application number 10-2006-0045 193, filed May 19, 2006, entitled "Separately Trained System and Method Using Two-Layered Neural Network with Target Values of Hidden Nodes". The benefit under 35 USC 119(a) of the Korean (Republic of) application is hereby claimed, and the aforementioned application is hereby incorporated herein by reference.
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