Implementation of a chemical reactor selection expert system in an artificial neural network

Abstract

Artificial neural networks can be used for implementing logical expressions, which from the bases of most expert systems. The aim of this work was to investigate the feasibility of using a feed-forward neural network for knowledge acquisition and storage, and subsequent use as a chemical reactor selection expert system. The network was trained by the Levenberg-Marquardt method to minimise the sum of squares of the residuals. The output of each node was calculated by the logistic activation (sigmoid) function on the weighted sum of inputs to that node. It is possible in certain cases like this one to interpret the functions of the nodes in the hidden layer. This work demonstrates that a selection expert system can be implemented in a feed-forward neural network. In other words, neural networks can be used for knowledge acquisition and storage for selection expert systems, suitable for convenient retrieval and inferencing. In spite of covering a wide range (several orders of magnitude) of inputs, the performance was very good.