Training Feedforward Neural Networks Using an Enhanced Marine Predators Algorithm

Training Feedforward Neural Networks Using Symbiotic Organisms Search Algorithm

Symbiotic organisms search (SOS) is a new robust and powerful metaheuristic algorithm, which stimulates the symbiotic interaction strategies adopted by organisms to survive and propagate in the ecosystem. In the supervised learning area, it is a challenging task to present a satisfactory and efficient training algorithm for feedforward neural networks (FNNs). In this paper, SOS is employed as a...

Training Feedforward Neural Networks Using Genetic Algorithms

Multilayered feedforward neural networks possess a number of properties which make them particularly suited to complex pattern classification problems. However, their application to some realworld problems has been hampered by the lack of a training algonthm which reliably finds a nearly globally optimal set of weights in a relatively short time. Genetic algorithms are a class of optimization p...

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...

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...