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Output Reachable Set Estimation and Verification for Multi-Layer Neural Networks

Abstract

In this brief, the output reachable estimation and safety verification problems for multilayer perceptron (MLP) neural networks are addressed. First, a conception called maximum sensitivity is introduced, and for a class of MLPs whose activation functions are monotonic functions, the maximum sensitivity can be computed via solving convex optimization problems. Then, using a simulation-based method, the output reachable set estimation problem for neural networks is formulated into a chain of optimization problems. Finally, an automated safety verification is developed based on the output reachable set estimation result. An application to the safety verification for a robotic arm model with two joints is presented to show the effectiveness of the proposed approaches.

Year of Publication
2018
Journal
IEEE Transactions on Neural Networks and Learning Systems
Volume
29
Number of Pages
5777-5783
DOI
10.1109/TNNLS.2018.2808470