A generalized TSK dynamic fuzzy neural network: application to adaptive noise cancellation

Presents a dynamic fuzzy neural network consisting of generalized TSK rules. The premise and defuzzification parts are static while the consequent parts are recurrent neural networks with internal feedback and time delay synapses. The network is trained by means of a novel learning algorithm, bused on the concept of constrained optimization. The suggested algorithm is general since it can be applied to locally as well as fully recurrent networks, regardless of their structure. An adaptation mechanism of the maximum parameter change is also developed. The proposed dynamic model, equipped with the learning algorithm, is employed as a noise cancellation filter, where it is compared with the ANFIS fuzzy filter. Simulation results show that the suggested model compares favorably with its competing rival and can be regarded as a reliable, general adaptive filter