𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Composite adaptive control with locally weighted statistical learning

✍ Scribed by Jun Nakanishi; Jay A. Farrell; Stefan Schaal


Publisher
Elsevier Science
Year
2005
Tongue
English
Weight
901 KB
Volume
18
Category
Article
ISSN
0893-6080

No coin nor oath required. For personal study only.


πŸ“œ SIMILAR VOLUMES


Adaptive neural control with stable lear
✍ S.J. Hepworth; A.L. Dexter πŸ“‚ Article πŸ“… 1996 πŸ› Elsevier Science 🌐 English βš– 697 KB

The paper considers the problem of training on-line a neural network model of non-linear heater battery for implementation in a model-based control scheme. A stable learning scheme is proposed which reduces parameter drift due to process-model mismatch in radial basis function (RBF) networks. A netw

Adaptive robust iterative learning contr
✍ Jian-Xin Xu; Badrinath Viswanathan πŸ“‚ Article πŸ“… 2000 πŸ› Elsevier Science 🌐 English βš– 194 KB

An adaptive robust iterative learning control method based on a new dead-zone scheme is presented for the control of nonlinear uncertain systems. The new dead-zone scheme ceases both learning and adaptation whenever the previous iteration error enters a pre-speci"ed error bound, in the sequel enhanc

Reinforcement learning combined with a f
✍ K.H. Quah; C. Quek; G. Leedham πŸ“‚ Article πŸ“… 2005 πŸ› Elsevier Science 🌐 English βš– 222 KB

Reinforcement learning has been widely-used for applications in planning, control, and decision making. Rather than using instructive feedback as in supervised learning, reinforcement learning makes use of evaluative feedback to guide the learning process. In this paper, we formulate a pattern class