Patent ID: 7734555

Claim:
A separate learning system using a two-layered neural network having target values for hidden nodes, comprising: a computer having a control unit; an input layer for receiving training data from a user, and including at least one input node; a hidden layer including at least one hidden node; a first connection weight unit for connecting the input layer to the hidden layer, and changing a weight between the input node and the hidden node, thus performing learning, which is expressed by: Δ ⁢ ⁢ w ih * = - η ⁢ ∂ E ∂ w ih * = η ⁢ ∑ j ⁢ ⁢ { ( d j - y j ) ⁢ S ′ ⁡ ( u j ) ⁢ w * hj } ⁢ S ′ ⁡ ( v h ) ⁢ x i wherein u j is the input value of the j-th output node, v h is the input value of the h-th hidden node, d j is a target value for a j-th output node, S″ is an activation function, x i is an i-th input, w ih* is a weight directed from an i-th input node to an h-th hidden node, w *hj is a weight directed from the h-th hidden node to the j-th output node, and y j is the output value of the j-th output node; an output layer for outputting training data that has been completely learned; a second connection weight unit for connecting the hidden layer to the output layer, changing a weight between the output and the hidden node, and calculating a target value for the hidden node, based on a current error for the output node, thus performing learning; and the control unit stopping learning, fixing the second connection weight unit, turning a learning direction to the first connection weight unit, and causing 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.