document: Identification of evolving fuzzy rule-based models An approach to identification of evolving fuzzy rule-based -LRB- eR -RRB- models is proposed . eR models implement a method for the noniterative update of both the rule-base structure and parameters by incremental unsupervised learning . The rule-base evolves by adding more informative rules than those that previously formed the model . In addition , existing rules can be replaced with new rules based on ranking using the informative potential of the data . In this way , the rule-base structure is inherited and updated when new informative data become available , rather than being completely retrained . The adaptive nature of these evolving rule-based models , in combination with the highly transparent and compact form of fuzzy rules , makes them a promising candidate for modeling and control of complex processes , competitive to neural networks . The approach has been tested on a benchmark problem and on an air-conditioning component modeling application using data from an installation serving a real building . The results illustrate the viability and efficiency of the approach
doc_bio_tags: B O O O O O O O O B O O O O O O O O O O O O O O O O O O B I O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O