New Scaled Conjugate Gradient Algorithm for Training Artificial Neural Networks Based on Pure Conjugacy Condition

Conjugate gradient algorithm for training neural networks

A conjugate gradient based method for Decision Neural Network training

Decision Neural Network is a new approach for solving multi-objective decision-making problems based on artificial neural networks. Using inaccurate evaluation data, network training has improved and the number of educational data sets has decreased. The available training method is based on the gradient decent method (BP). One of its limitations is related to its convergence speed. Therefore,...

New Conjugacy Condition and Its Resulting Nonlinear Conjugate Gradient Methods

Conjugate gradient methods are a class of important methods for unconstrained optimization, especially when the dimension is large. This paper proposes a new conjugacy condition, which considers an inexact line search scheme but reduces to the old one if the line search is exact. Based on the new conjugacy condition, two nonlinear conjugate gradient methods are constructed. Convergence analysis...

Scaled Conjugate Gradient Learning for Quaternion-Valued Neural Networks

Conjugate Gradient Methods in Training Neural Networks

Training of artificial neural networks is normally a time consuming task due to iterative search imposed by the implicit nonlinearity of the network behavior. To tackle the supervised learning of multilayer feed forward neural networks, the backpropagation algorithm has been proven to be one of the most successful neural network algorithm. Although backpropagation training has proved to be effi...