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.
In REF , a concept called maximal sensitivity is introduced to compute the reachable set estimation for feedforward neural networks with a broader class of activation functions satisfying a monotonic assumption.
Output Reachable Set Estimation and Verification for Multilayer Neural Networks
IEEE Transactions on Neural Networks and Learning Systems
IEEE Transactions on Neural Networks and Learning Systems
Mathematics Medicine Computer Science