An Overview of Sequential Learning Algorithms for Single Hidden Layer Networks: Current Issues & Future Trends
Year: 2020
Type: preprint
Abstract: This paper serves as an overview for sequential learning algorithms for single hidden layer neural nets. Cite as: M. H. Arshad, M. A. Abido. An Overview of Sequential Learning Algorithms for Single Hidden Layer Networks: Current Issues & Future Trends. Abstract: In this paper, a brief survey of the commonly used sequential-learning algorithms used with single hidden layer feed-forward neural networks is presented. A glimpse at the different kinds that are available in the literature up until now, how they have developed throughout the years, and their relative execution is summarized. Most important things to take note of during the designing phase of neural networks are its complexity, computational efficiency, maximum training time, and ability to generalize the under-study problem. The comparison of different sequential learning algorithms in regard to these merits for single hidden layer neural networks is drawn.
Authors: Muhammad Haseeb Arshad, M. A. Abido
Institution: King Fahd University of Petroleum and Minerals
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4238802147