Mastorocostas, ParisVarsamis, DimitrisHilas, ConstantinosMastorocostas, Constantinos2015-06-252024-09-272015-06-252024-09-272008-101433-3058http://link.springer.com/article/10.1007/s00521-007-0129-3https://repository2024.ihu.gr/handle/123456789/1497This paper presents a recurrent fuzzy-neural filter for adaptive noise cancelation. The cancelation task is transformed to a system-identification problem, which is tackled by use of the dynamic neuron-based fuzzy neural network (DN-FNN). The fuzzy model is based on Takagi–Sugeno–Kang fuzzy rules, whose consequent parts consist of linear combinations of dynamic neurons. The orthogonal least squares method is employed to select the number of rules, along with the number and kind of dynamic neurons that participate in each rule. Extensive simulation results are given and performance comparison with a series of other dynamic fuzzy and neural models is conducted, underlining the effectiveness of the proposed filter and its superior performance over its competing rivals.9enAttribution-NonCommercial-NoDerivatives 4.0 Διεθνέςhttp://creativecommons.org/licenses/by-nc-nd/4.0/Άρθρο σε επιστημονικό περιοδικό10.1007/s00521-007-0129-3Recurrent fuzzy-neural modelingDynamic neuronsAdaptive noise cancelation