Controlling with words using automatically identified fuzzy Cartesian granule feature models

We present a new approach to representing and acquiring controllers based u pon Cartesian granule features - multidimensional features formed over the cross product of words drawn from the linguistic partitions of the constitu ent input features - incorporated into additive models. Controllers express ed in terms of Cartesian granule features enable the paradigm "controlling with words" by translating process data into words that are subsequently us ed to interrogate a rule base, which ultimately results in a control action . The system identification of good, parsimonious additive Cartesian granul e feature models is an exponential search problem. In this paper we present the G_DACG constructive induction algorithm as a means of automatically id entifying additive Cartesian granule feature models from example data. G_DA CG combines the powerful optimisation capabilities of genetic programming w ith a novel and cheap fitness function, which relies on the semantic separa tion of concepts expressed in terms of Cartesian granule fuzzy sets, in ide ntifying these additive models. We illustrate the approach on a variety of problems including the modelling of a dynamical process and a chemical plan t controller. (C) 1999 Elsevier Science Inc. All rights reserved.