TY - JOUR
T1 - Feedforward backpropagation artificial neural networks on reconfigurable meshes
AU - Jenq, John Jing Fu
AU - Li, Wingning
PY - 1998/12
Y1 - 1998/12
N2 - 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.
AB - 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.
KW - Artificial neural networks
KW - Parallel algorithms
KW - Reconfigurable mesh algorithms
UR - http://www.scopus.com/inward/record.url?scp=0032317735&partnerID=8YFLogxK
U2 - 10.1016/s0167-739x(98)00036-3
DO - 10.1016/s0167-739x(98)00036-3
M3 - Article
AN - SCOPUS:0032317735
SN - 0167-739X
VL - 14
SP - 313
EP - 319
JO - Future Generation Computer Systems
JF - Future Generation Computer Systems
IS - 5-6
ER -