Please use this identifier to cite or link to this item:
|Title:||Adaptive learning control for finite interval tracking based on constructive function approximation and wavelet|
|Authors:||Xu, J.-X. |
|Citation:||Xu, J.-X., Yan, R. (2011-06). Adaptive learning control for finite interval tracking based on constructive function approximation and wavelet. IEEE Transactions on Neural Networks 22 (6) : 893-905. ScholarBank@NUS Repository. https://doi.org/10.1109/TNN.2011.2132143|
|Abstract:||Using a constructive function approximation network, an adaptive learning control (ALC) approach is proposed for finite interval tracking problems. The constructive function approximation network consists of a set of bases, and the number of bases can evolve when learning repeats. The nature of the basis allows the continuous adaptive learning of parameters when the network undergoes any structural changes, and consequently offers the flexibility in tuning the network structure. The expandability of the bases guarantees precision of the function approximation and avoids the trial-and-error procedure in structure selection for any fixed structure network. Two classes of unknown nonlinear functions, namely, either global L2 or local L2 with a known bounding function, are taken into consideration. Using the Lyapunov method, the existence of solution and the convergence property of the proposed ALC system are discussed in a rigorous manner. By virtue of the celebrated orthonormal and multiresolution properties, wavelet network is used as the universal function approximator, with the weights tuned by the proposed adaptive learning mechanism. © 2011 IEEE.|
|Source Title:||IEEE Transactions on Neural Networks|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Mar 19, 2019
WEB OF SCIENCETM
checked on Mar 4, 2019
checked on Jan 12, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.