FLOW OF INFORMATION THROUGH AN ARTIFICIAL NEURAL-NETWORK

The patterns of information flow through an artificial neural network are examined in terms of how and why a network characterises input/out put relationships, and what insight these patterns give as to the char acteristics of the network that could be changed to improve its descri ption of a system. The prediction of vapour-liquid equilibrium in term s of bubble-point conditions is used as a case study and shows that th e network is capable of identifying the intrinsic characteristics of t he system. However the accuracy of the prediction depends on the regio n of the input/output data space considered, drawing attention to the difficulties encountered by the empirical structuring of a network. Th e ability to identify the type and strength of the relationships betwe en process variables indicates that a priori knowledge of the system c ould be used to relate parts of the network to dominant elements of th e intrinsic model. This implies there could be advantages to be gained by exploiting knowledge of the system to maximise the information con tent captured by the network and establish a systematic way of designi ng its structure.