Aj. Meade et Ba. Zeldin, ESTABLISHING CRITERIA TO ENSURE SUCCESSFUL FEEDFORWARD ARTIFICIAL NEURAL-NETWORK MODELING OF MECHANICAL SYSTEMS, Mathematical and computer modelling, 27(5), 1998, pp. 61-74
The emulation of mechanical systems is a popular application of artifi
cial neural networks in engineering. This paper examines general princ
iples of modelling mechanical systems by feedforward artificial neural
networks (FFANNs). The slow convergence issue associated with the hig
hly parallel and redundant structure of FFANN systems is addressed by
formulating criteria for constraining network parameters so that FFANN
s may be reliably applied to mechanics problems. The existence of the
FFANN mechanical model and its stability during construction, with res
pect to the error in the data, are analyzed. Also, a class of differen
tial equations is analyzed for use with Tikhonov regularization. It is
shown that the use of Tikhonov regularization can aid in FFANN data-d
riven construction with a priori mathematical models of varying degree
s of physical fidelity. Criteria to ensure successful FFANN applicatio
n from an engineering perspective are established.