Generalized Dai-Yuan conjugate gradient algorithm for training multi-layer feed-forward neural networks

Conjugate gradient algorithm for training neural networks

Training Feed-Forward Neural Networks Using Conjugate Gradients

Figure 2: Scatter plot of testing results vs. training results for 32-24-10 networks, late stopping. Open circles: = 0; lled circles: = 10 03 .

A Dai-Yuan conjugate gradient algorithm with sufficient descent and conjugacy conditions for unconstrained optimization

A modification of the Dai-Yuan conjugate gradient algorithm is proposed. Using the exact line search, the algorithm reduces to the original version of the Dai and Yuan computational scheme. For inexact line search the algorithm satisfies both the sufficient descent and conjugacy condition. A global convergence result is proved when the Wolfe line search conditions are used. Computational result...

Introduction to multi-layer feed-forward neural networks

Basic definitions concerning the multi-layer feed-forward neural networks are given. The back-propagation training algorithm is explained. Partial derivatives of the objective function with respect to the weight and threshold coefficients are derived. These derivatives are valuable for an adaptation process of the considered neural network. Training and generalisation of multi-layer feed-forwar...

Training Feed-Forward Neural Networks Using Firefly Algorithm

In this work, firefly algorithm (FA) is used in training feed-forward neural networks (FNN) for classification purpose. In experiments, three well-known classification problems have been used to evaluate the performance of the proposed FA. The experimental results obtained by FA were compared with the results reported by artificial bee colony (ABC) algorithm and genetic algorithm (GA). Also, si...