Neurofuzzy modeling of chemical process systems with ellipsoidal radial basis function neural networks and genetic algorithms

Abstract

Non-parametric methods for the construction of empirical process models have been used successfully in a variety of contexts in the field of process engineering. Despite their ability to form accurate representations of chemical process systems, non-parametric models are usually difficult to interpret. This is a serious hindrance where a premium is placed on model reliability and transparency. In this paper it is shown that by making use of radial basis function neural networks with arbitrarily oriented ellipsoidal basis functions, more parsimonious process models can be constructed. As with other radial basis function neural networks, the radial basis kernels also lend themselves to the construction of fuzzy rules. The methodology is illustrated by means of a case study on induced aeration in agitated vessels.