2 citations · 6 across the 4 of their papers we have counts for
4 papers
Approximation with Random Shallow ReLU Networks with Applications to Model Reference Adaptive Control
Andrew Lamperski, Tyler Lekang
Neural networks are regularly employed in adaptive control of nonlinear systems and related methods of reinforcement learning. A common architecture uses a neural network with a si…
Function Approximation with Randomly Initialized Neural Networks for Approximate Model Reference Adaptive Control
Tyler Lekang, Andrew Lamperski
Classical results in neural network approximation theory show how arbitrary continuous functions can be approximated by networks with a single hidden layer, under mild assumptions…
Bounds on stationary moments in stochastic chemical kinetics
Khem Raj Ghusinga, Cesar A. Vargas-Garcia, Andrew Lamperski +1
In the stochastic formulation of chemical kinetics, the stationary moments of the population count of species can be described via a set of linear equations. However, except for so…
Automata Theory Meets Barrier Certificates: Temporal Logic Verification of Nonlinear Systems
Tichakorn Wongpiromsarn, Ufuk Topcu, Andrew Lamperski
We consider temporal logic verification of (possibly nonlinear) dynamical systems evolving over continuous state spaces. Our approach combines automata-based verification and the u…