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|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|
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