Feedforward backpropagation artificial neural networks on reconfigurable meshes

TY - JOUR

T1 - Feedforward backpropagation artificial neural networks on reconfigurable meshes

AU - Jenq, John Jing Fu

AU - Li, Wingning

PY - 1998/12/1

Y1 - 1998/12/1

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

M3 - Article

SN - 0167-739X

VL - 14

SP - 313

EP - 319

JO - Future Generation Computer Systems

JF - Future Generation Computer Systems

IS - 5-6

ER -