6 papers
Observer-Based Safety Monitoring of Nonlinear Dynamical Systems with Neural Networks via Quadratic Constraint Approach
Tao Wang, Yapeng Li, Zihao Mo +2
The safety monitoring for nonlinear dynamical systems with embedded neural network components is addressed in this paper. The interval-observer-based safety monitor is developed co…
Efficient Neural Hybrid System Learning and Transition System Abstraction for Dynamical Systems
Yejiang Yang, Zihao Mo, Weiming Xiang
This paper proposes a neural network hybrid modeling framework for dynamics learning to promote an interpretable, computationally efficient way of dynamics learning and system iden…
A Transition System Abstraction Framework for Neural Network Dynamical System Models
Yejiang Yang, Zihao Mo, Hoang-Dung Tran +1
This paper proposes a transition system abstraction framework for neural network dynamical system models to enhance the model interpretability, with applications to complex dynamic…
Compression Repair for Feedforward Neural Networks Based on Model Equivalence Evaluation
Zihao Mo, Yejiang Yang, Shuaizheng Lu +1
In this paper, we propose a method of repairing compressed Feedforward Neural Networks (FNNs) based on equivalence evaluation of two neural networks. In the repairing framework, a…
Guaranteed Quantization Error Computation for Neural Network Model Compression
Wesley Cooke, Zihao Mo, Weiming Xiang
Neural network model compression techniques can address the computation issue of deep neural networks on embedded devices in industrial systems. The guaranteed output error computa…
A Data-Driven Hybrid Automaton Framework to Modeling Complex Dynamical Systems
Yejiang Yang, Zihao Mo, Weiming Xiang
In this paper, a computationally efficient data-driven hybrid automaton model is proposed to capture unknown complex dynamical system behaviors using multiple neural networks. The…