Abstract
This paper proposes the type-2 fuzzy neural network system (type-2 FNN) which combines the advantages of type-2 fuzzy logic systems (FLSs) and neural networks (NNs). For considering the system uncertainties, we use the type-2 FLSs to develop a type-2 FNN system. The previous results of type-1 FNN systems can be extended to a type-2 one. Furthermore, the corresponding learning algorithm is derived by input-to-state-stability (ISS) approach. Nonlinear system identification is presented to illustrate the effectiveness of our approach.
This work was support partially by National Science Council, Taiwan, R.O.C. under NSC-94-2213-E-155- 039.
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Lee, CH., Lin, YC. (2007). Type-2 Fuzzy Neuro System Via Input-to-State-Stability Approach. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_39
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DOI: https://doi.org/10.1007/978-3-540-72393-6_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72392-9
Online ISBN: 978-3-540-72393-6
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