Yuan Wang

TL;DR: The finite time synchronization problem of fractional order quaternion valued neural networks with time delay is investigated through using Lyapunov direct method and the setting time is estimated, which is influenced by the order of fractionAL derivative and control parameters.

TL;DR: By applying Lyapunov direct method rather than decomposition method, quaternion projective synchronization of FODQVNN is discussed, in which the projective coefficient is set to quaternions number as discussed by the authors.

TL;DR: In this paper , the authors present an end-to-end framework for solving job shop scheduling problems by using graph neural networks (GNNs) and deep Q-learning, which is suitable for solving instances that have similar sizes and is trained only by observing reward signals and following feasible rules.

TL;DR: The proposed QVRNNs are robust in terms of the design parameter selection and neurons are reduced, and by virtue of the geometrical properties of the activation function and the fixed point theorem, several inequalities are given to guarantee the global exponential stability for the Q VRNNs.

TL;DR: Zhang et al. as discussed by the authors investigated the impact of resource misallocation on total factor productivity in China and concluded that the contribution of capital and labor distortion to the overall productivity is highest in the eastern region of China with −0.036 and 0.065, respectively, followed by the northeast, central, and western regions.