Feedforward Backpropagation Artificial Neural Networks on Reconfigurable Meshes
Document Type
Article
Publication Date
12-1-1998
Abstract
The artificial neural networks (ANNs) have been used successfully in applications such as pattern recognition, image processing, automation and control. Majority of today's applications use backpropagate feedforward ANN. In this paper, two methods of P pattern L layer ANN learning on n × n RMESH have been presented. One required memory space of O(nL) but conceptually is simpler to develop and the other uses pipelined approach which reduces the memory requirement to O(L). Both of these algorithms take O(PL) time and are optimal for RMESH architecture.
Montclair State University Digital Commons Citation
Jenq, John and Li, Wing Ning, "Feedforward Backpropagation Artificial Neural Networks on Reconfigurable Meshes" (1998). Department of Computer Science Faculty Scholarship and Creative Works. 292.
https://digitalcommons.montclair.edu/compusci-facpubs/292