Exact(14)
The proposed adaptive gain controller, which results from the direct adaptive approach, has the ability to tune the adaptation parameter in the THEN-part of each fuzzy rule during real-time operation.
Additionally, an adaptation parameter based on comparison between model predictions and previous experimental results was added.
In Section 2.5, the estimation of the prior distribution of the adaptation parameter is described.
The effects of parameter adaptation, parameter sensitivity and local search method are studied.
The effects of parameter adaptation, parameter sensitivity and proposed mechanism are discussed in detail.
Each fuzzy rule corresponds to a sub-wavelet neural network (sub-WNN) and one adaptation parameter.
Similar(45)
LMI-based methods using S-variables allow to design a robust adaptive controller with tunable gain adaptation parameters.
It comprises a syntax for adaptivity rules based on a set of adaptation parameters.
As a result, the proposed adaptive controller has a simpler form and requires fewer adaptation parameters.
Asymptotic stability of the given adaptive control is proved using Lyapunov arguments, and gain adaptation parameters are tunable by linear matrix inequality based convex optimization.
The fit at 32°C yields the same adaptation parameters as obtained from fitting dose-response curves of adapting cells [ 47].
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