Search Results for author:
Found 2 papers, 1 papers with code
Deep Compression of Neural Networks for Fault Detection on Tennessee Eastman Chemical Processes
Artificial neural network has achieved the state-of-art performance in fault detection on the Tennessee Eastman process, but it often requires enormous memory to fund its massive parameters.
Convex Relaxations for Global Optimization Under Uncertainty Described by Continuous Random Variables
To address this, this article presents a new method for computing convex and concave relaxations of nonconvex expected-value functions, which can be used to obtain rigorous bounds for use in B&B.
Optimization and Control
Cannot find the paper you are looking for? You can
Submit a new open access paper.
