An Evolving Fuzzy Model for Embedded Applications

Abstract

This paper describes an evolving fuzzy model (efM) approach to modelling non-linear dynamic systems in which an incremental learning method is used to build up the rule-base. The rule-base evolves when "new" information becomes available by creating a new rule, merging an existing rule or deleting an old rule, depended upon the proximity and potential of the rules, and the maximum number of rules to be used in the rule-base. The efM, which is based on a T-S fuzzy model with constant consequents, is a very good candidate for modelling complex non-linear systems, when the period of time required to collect a complete set of training data is too long for the model to be identified off-line and the learning scheme must be computationally undemanding, e.g. use in model-based self-learning controllers. The results presented in the paper demonstrate the ability of the efM to evolve the rule-base efficiently so as to account for the behaviour of the system in new regions of the operating space. The proposed approach generates an accurate model with relatively few rules in a computationally undemanding manner, even if the data are incomplete