Towards a Very Fast Feedforward Multilayer Neural Networks Training Algorithm

EAST: An Exponential Adaptive Skipping Training Algorithm for Multilayer Feedforward Neural Networks

Multilayer Feedforward Neural Network (MFNN) has been administered widely for solving a wide range of supervised pattern recognition tasks. The major problem in the MFNN training phase is its long training time especially when it is trained on very huge training datasets. In this accordance, an enhanced training algorithm called Exponential Adaptive Skipping Training (EAST) Algorithm is propose...

A new training algorithm for feedforward neural networks

A Constructive Algorithm for Feedforward Neural Networks With Incremental Training

We develop, in this brief, a new constructive learning algorithm for feedforward neural networks. We employ an incremental training procedure where training patterns are learned one by one. Our algorithm starts with a single training pattern and a single hidden-layer neuron. During the course of neural network training, when the algorithm gets stuck in a local minimum, we will attempt to escape...

Selective Learning for Multilayer Feedforward Neural Networks

Evolutional Design and Training Algorithm for Feedforward Neural Networks

In pattern recognition using neural networks, it is very difficult for researchers or users to design optimal neural network architecture for a specific task. It is possible for any kinds of neural network architectures to obtain a certain measure of recognition ratio. It is, however, difficult to get an optimal neural network architecture for a specific task analytically in the recognition rat...