This paper suggests that a simple modification to the initial search direction can also substantially improve the training efficiency of almost all major optimization methods. It was discovered that if the initial search direction is locally modified by a gain value used in the activation function of the corresponding node, significant improvements in the convergence rates can be achieved irrespective of the optimization algorithm used. Furthermore the proposed method is robust, easy to compute, and easy to implement into well known nonlinear conjugate gradient algorithms

An Improved Learning Algorithm Based on The Broyden-Fletcher-Goldfarb- Shanno (BFGS) Method For Back Propagation Neural Networks.pdf

• We are an exchange download platform that only provides communication channels. The downloaded content comes from the internet. Except for download issues, please Google on your own.
• The downloaded content is provided for members to upload. If it unintentionally infringes on your copyright, please contact us.
• Please use Winrar for decompression tools
• If download fail, Try it againg or Feedback to us.
• If downloaded content did not match the introduction, Feedback to us，Confirm and will be refund.
• Before downloading, you can inquire through the uploaded person information

Nothing．