Abstract
In this paper, we propose a online clustering fuzzy neural network. The proposed neural fuzzy network uses the online clustering to train the structure, the gradient to train the parameters of the hidden layer, and the Kalman filter algorithm to train the parameters of the output layer. In our algorithm, learning structure and parameter learning are updated at the same time, we do not make difference in structure learning and parameter learning. The center of each rule is updated to obtain the center is near to the incoming data in each iteration. In this way, it does not need to generate a new rule in each iteration, i.e., it neither generates many rules nor need to prune the rules. We prove the stability of the algorithm.
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Rubio, J.J., Pacheco, J. An stable online clustering fuzzy neural network for nonlinear system identification. Neural Comput & Applic 18, 633–641 (2009). https://doi.org/10.1007/s00521-009-0289-4
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DOI: https://doi.org/10.1007/s00521-009-0289-4