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In the vCAAS model, an adaptive virtual interface (VIF) is created for each virtual node (VM).
To efficiently solve this model, an adaptive large neighborhood search (ALNS) algorithm is then designed.
Based on this model, an adaptive fuzzy observer is developed to deal with the synchronization of nonidentical chaotic systems.
For our FTS model, an adaptive neuro-fuzzy inference system is constructed by considering the input parameters (lambda) and (nu) and one output (E[N tau)]).
With this model, an adaptive and fast SR method based on dyadic wavelet transform and least square system parameters solving is proposed in this paper.
In order to solve the UBP model, an Adaptive Genetic Algorithm (AGA) using crossing over and mutation rates that are automatically tuned in each generation is proposed.
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The suggested MRAS speed observer employed the current model as an adaptive model.
Based on the dynamic fuzzy neural network (DFNN) modeling, an adaptive backstepping controller is devised, which works through structure and parameter-learning phases for adaptation.
Based on the estimated fuzzy models, an adaptive controller, which works through the sliding mode control, is designed to make the system track the desired unstable periodic orbits.
Objectives: The objective of the study was to compare two electroencephalographic (EEG) derived depth of anesthesia indexes that were developed with two different models: an adaptive neuro-fuzzy inference system (qCON ANFIS) and a quadratic equation model (qCON QE).
On the basis of these models, an adaptive state regulator and an adaptive full-state observer (Luenberger Observer) are designed for vibration suppression using the optimal linear quadratic regulator (LQR).
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