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The increasing prevalence of neural networks in safety-critical controlsystems underscores the imperative need for rigorous methods to ensure thereliability and safety of these systems. This work introduces a novel approachemploying hybrid zonotopes to compute the over-approximation of backwardreachable sets for neural feedback systems with nonlinear system models andgeneral activation functions. Closed-form expressions as hybrid zonotopes areprovided for the over-approximated backward reachable sets, and a refinementprocedure is proposed to alleviate the potential conservatism of theapproximation. Two numerical examples are provided to illustrate theeffectiveness of the proposed approach.

Hybrid Zonotope-Based Backward Reachability Analysis for Neural Feedback Systems With Nonlinear System Models