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Creators/Authors contains: "Mo, Zihao"

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  1. ; ; (, ASME Letters in Dynamic Systems and Control)
    Abstract This article proposes a neural network hybrid modeling framework for dynamics learning to promote an interpretable, computationally efficient method of dynamics learning and system identification. First, a low-level model is trained to learn the system dynamics, which utilizes multiple simple neural networks to approximate the local dynamics generated from data-driven partitions. Then, based on the low-level model, a high-level model is trained to abstract the low-level neural hybrid system model into a transition system that allows computational tree logic (CTL) verification to promote model’s ability to handle human interaction and verification efficiency. 
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    Free, publicly-accessible full text available January 1, 2026
  2. ; ; (, IEEE)
    Free, publicly-accessible full text available December 18, 2025
  3. ; ; ; (, IEEE)
    Free, publicly-accessible full text available December 18, 2025
  4. ; ; ; ; (, International Journal of Control)
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  5. ; ; ; (, IEEE)
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  6. ; ; ; (, IEEE)
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  7. ; (, Information Sciences)
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  8. ; ; (, IFAC-PapersOnLine)
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  9. ; ; (, 2023 IEEE International Conference on Industrial Technology (ICIT))
    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 sampled data of the system is divided by valid partitions into groups corresponding to their topologies and based on which, transition guards are defined. Then, a collection of small-scale neural networks that are computationally efficient are trained as the local dynamical description for their corresponding topologies. After modeling the system with a neural-network-based hybrid automaton, the set-valued reachability analysis with low computation cost is provided based on interval analysis and a split and combined process. At last, a numerical example of the limit cycle is presented to illustrate that the developed models can significantly reduce the computational cost in reachable set computation without sacrificing any modeling precision. 
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  10. ; ; (, 2023 IEEE International Conference on Industrial Technology (ICIT))
    Neural network model compression techniques can address the computation issue of deep neural networks on embedded devices in industrial systems. The guaranteed output error computation problem for neural network compression with quantization is addressed in this paper. A merged neural network is built from a feedforward neural network and its quantized version to produce the exact output difference between two neural networks. Then, optimization-based methods and reachability analysis methods are applied to the merged neural network to compute the guaranteed quantization error. Finally, a numerical example is proposed to validate the applicability and effectiveness of the proposed approach. 
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