Parallel processing of chemical information in a local area network--II. A parallel cross-validation procedure for artificial neural networks.
This paper describes a parallel cross-validation (PCV) procedure, for testing the predictive ability of multi-layer feed-forward (MLF) neural networks models, trained by the generalized delta learning rule. The PCV program has been parallelized to operate in a local area computer network. Development and execution of the parallel application was aided by the HYDRA programming environment, which is extensively described in Part I of this paper. A brief theoretical introduction on MLF networks is given and the problems, associated with the validation of predictive abilities, will be discussed. Furthermore, this paper comprises a general outline of the PCV program. Finally, the parallel PCV application is used to validate the predictive ability of an MLF network modeling a chemical non-linear function approximation problem which is described extensively in the literature.
Derks
E P
EP
Laboratory for Analytical Chemistry, Faculty of Science, Catholic University of Nijmegen, The Netherlands.
Beckers
M L
ML
Melssen
W J
WJ
Buydens
L M
LM
D001185:Artificial Intelligence; D003198:Computer Simulation; D016208:Databases, Factual; D008122:Local Area Networks; D008956:Models, Chemical; D011381:Programming Languages; D011786:Quality Control