Abstract
This paper deals with the use of a special kind of recurrent neuro-fuzzy model to represent complex systems. The neuro-fuzzy system, called RFasArt, has been used in this work to model a complex bio technological process: an activated sludge process taken from a real wastewater treatment plant. This network is based on the adaptive resonance theory (ART) but it also introduces formalisms from the fuzzy set theory and takes into account the available contextual information in its processing stage. Real data records taken from the plant were used to train this network. The results obtained with this recurrent fuzzy neural network have been compared with the ones obtained with a classical recurrent neural network, showing the advantageous behaviour of the RFasArt system. Apart from modelling, the RFasArt architecture provides a knowledge base of fuzzy rules containing information about the plant dynamic behaviour.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
