Fuzzy wavelet networks for function learning
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 200-211 |
| Journal / Publication | IEEE Transactions on Fuzzy Systems |
| Volume | 9 |
| Issue number | 1 |
| Publication status | Published - Feb 2001 |
Link(s)
Abstract
Inspired by the theory of multiresolution analysis (MRA) of wavelet transforms and fuzzy concepts, a fuzzy wavelet network (FWN) is proposed for approximating arbitrary nonlinear functions in this paper. The FWN consists of a set of fuzzy rules. Each rule corresponding to a sub-wavelet neural network (WNN) consists of single-scaling wavelets. Through efficient bases selection, the dimension of the approximated function does not cause the bottleneck for constructing FWN. Especially, by learning the translation parameters of the wavelets and adjusting the shape of membership functions, the model accuracy and the generalization capability of the FWN can be remarkably improved. Furthermore, an algorithm for constructing and training the fuzzy wavelet networks is proposed. Simulation examples are also given to illustrate the effectiveness of the method.
Research Area(s)
- Fuzzy neural networks, Wavelet neural networks, Wavelet transforms
Citation Format(s)
Fuzzy wavelet networks for function learning. / Ho, Daniel W. C.; Zhang, Ping-An; Xu, Jinhua.
In: IEEE Transactions on Fuzzy Systems, Vol. 9, No. 1, 02.2001, p. 200-211.
In: IEEE Transactions on Fuzzy Systems, Vol. 9, No. 1, 02.2001, p. 200-211.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
