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eess.SY2026
Local Observability and Moving Horizon Estimation-based Training of Feedforward Neural Networks
Yi Yang, Victor G. Lopez, Matthias A. Müller
In this paper, we propose a moving horizon estimation (MHE)-based training method for feedforward neural networks (FNNs) with rectified linear unit (ReLU) activation functions to d…
eess.SY2025
Local Observability of a Class of Feedforward Neural Networks
Yi Yang, Victor G. Lopez, Matthias A. Müller
Beyond the traditional neural network training methods based on gradient descent and its variants, state estimation techniques have been proposed to determine a set of ideal weight…
eess.SY2025
A strictly predefined-time convergent and anti-noise fractional-order zeroing neural network for solving time-variant quadratic programming in kinematic robot control
Yi Yang, Xiao Li, Xuchen Wang +5
This paper proposes a strictly predefined-time convergent and anti-noise fractional-order zeroing neural network (SPTC-AN-FOZNN) model, meticulously designed for addressing time-va…