Notice of RetractionA new neural network algorithm based on conjugate gradient and output weight optimization

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

On the foundation of the three layers fully connected neural network model, this paper proposed a new algorithm which called output weight optimization-conjugate gradient algorithm (OWO-CG) based on the combination of the output weight optimization algorithm (OWO) and conjugate gradient algorithm (CG). Every time of the learning process is divided into two steps: the first step, use conjugate gradient optimization method to calculate learning factor, and then only modify the weights of input layer to hidden layer; the second step, use the output of hidden layer units to construct and solve linear equations to calculate the weights of output layer. Experimental results show that the new algorithm has greatly improved the training speed compared to the gradient descent algorithms, conjugate gradient algorithm and output weight optimization.