title: Parallel processing of chemical information in a local area network--II. A parallel cross-validation procedure for artificial neural networks.
abstract: 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.
authors.affiliation: Laboratory for Analytical Chemistry, Faculty of Science, Catholic University of Nijmegen, The Netherlands.
authors.forename: E P
authors.initials: EP
authors.lastname: Derks
authors.forename: M L
authors.initials: ML
authors.lastname: Beckers
authors.forename: W J
authors.initials: WJ
authors.lastname: Melssen
authors.forename: L M
authors.initials: LM
authors.lastname: Buydens
mesh_terms: D001185:Artificial Intelligence; D003198:Computer Simulation; D016208:Databases, Factual; D008122:Local Area Networks; D008956:Models, Chemical; D011381:Programming Languages; D011786:Quality Control