Introduces CLBC strategy with high-order tuners and composite learning for exponential stability and parameter convergence under IE or partial IE, using extra prediction error for transients.
Identi- fiability implies robust, globally exponentially convergent on-line pa- rameter estimation
3 Pith papers cite this work, alongside 4 external citations. Polarity classification is still indexing.
3
Pith papers citing it
4
external citations · external index
verdicts
UNVERDICTED 3representative citing papers
Characterizes Nash equilibria for MMV portfolio problems via FBSDEs and extended HJBs, with MMV equilibria investing more than MV ones and gap narrowing over time.
Active learning for optimal experimental design in ML-based building energy system identification yields up to 54% lower RMSE than passive random sampling on the BOPTEST simulator across neural network and Gaussian process models.
citing papers explorer
-
Composite learning control with modular backstepping and high-order tuners
Introduces CLBC strategy with high-order tuners and composite learning for exponential stability and parameter convergence under IE or partial IE, using extra prediction error for transients.
-
Time-consistent portfolio selection with monotone mean-variance preferences
Characterizes Nash equilibria for MMV portfolio problems via FBSDEs and extended HJBs, with MMV equilibria investing more than MV ones and gap narrowing over time.
-
Active Learning for Optimal Experimental Design in Machine Learning-Based Building Energy System Identification
Active learning for optimal experimental design in ML-based building energy system identification yields up to 54% lower RMSE than passive random sampling on the BOPTEST simulator across neural network and Gaussian process models.