Online system identification based on a state-space neuro-fuzzy system

Paulo Gil, Tiago Oliveira, L. Brito Palma

Research output: Contribution to conferencePaper

1 Citation (Scopus)

Abstract

In this paper a new general recurrent state-space~\acl{NF} model structure based on the combination of a modified Jordan network and an~\acl{ANFIS} is proposed. The Neural-Fuzzy System's online training relies on a Constrained Unscented Kalman Filter, where weights, rules, membership functions and consequents are recursively updated. Results from a benchmark MIMO system demonstrate the applicability and effectiveness of the proposed framework.
Original languageEnglish
Pages1-6
Number of pages6
DOIs
Publication statusPublished - 2017
Event2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS) - Otsu, Japan
Duration: 27 Jun 201730 Jun 2017

Conference

Conference2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS)
Period27/06/1730/06/17

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    Gil, P., Oliveira, T., & Palma, L. B. (2017). Online system identification based on a state-space neuro-fuzzy system. 1-6. Paper presented at 2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS), . https://doi.org/10.1109/IFSA-SCIS.2017.8023249