Petri nets (HPNs) that can model biological systems. In particular, based on a state space formulation we develop a decisionaided adaptive gradient descent (DAAGD) algorithm capable of cost-effectively estimating the parameters used in an HPN model. Contrary to standard gradient descent techniques, the DAAGD algorithm does not require prior knowledge, i.e., information about the discrete […]
An Adaptive Learning Approach To Parameter Estimation For Hybrid Petri Nets In Systems Biology
