With the overwhelming success in the field of quantum computing, much attention has been paid to constructing a quantum neural network by combining a classical neural network with quantum computing. In this paper, we ...
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With the overwhelming success in the field of quantum computing, much attention has been paid to constructing a quantum neural network by combining a classical neural network with quantum computing. In this paper, we propose a novel quantum neural network model based on a quantum version of the sigmoid function, which skillfully combines the non-linear dissipation dynamics of neural computation with the linear unitary dynamics of quantum computation. Moreover, we also add connections from the input layer to the output layer to increase the non-linear expression ability of the network and the similarity to the human brain's information processing. The specific steps and relevant formulas of the conjugate gradient algorithm in the learning stage of the quantum network parameters are also given in this paper. Finally, the feasibility and properties of the model are demonstrated by MATLAB simulation with a encryption and decryption experiment.
Our work is devoted to a class of optimal control problems of parabolic partial differential equations. Because of the partial differential equations constraints, it is rather difficult to solve the optimization probl...
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Our work is devoted to a class of optimal control problems of parabolic partial differential equations. Because of the partial differential equations constraints, it is rather difficult to solve the optimization problem. The gradient of the cost function can be found by the adjoint problem approach. Based on the adjoint problem approach, the gradient of cost function is proved to be Lipschitz continuous. An improved conjugate method is applied to solve this optimization problem and this algorithm is proved to be convergent. This method is applied to set-point values in continuous cast secondary cooling zone. Based on the real data in a plant, the simulation experiments show that the method can ensure the steel billet quality. From these experiment results, it is concluded that the improved conjugate gradient algorithm is convergent and the method is effective in optimal control problem of partial differential equations.
The performance of adaptive beamforming techniques is limited by the nonhomogeneous clutter scenario. An augmented Krylov subspace method is proposed, which utilizes only a single snapshot of the data for adaptive pro...
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The performance of adaptive beamforming techniques is limited by the nonhomogeneous clutter scenario. An augmented Krylov subspace method is proposed, which utilizes only a single snapshot of the data for adaptive processing. The novel algorithm puts together a data preprocessor and adaptive Krylov subspace algorithm, where the data preprocessor suppresses discrete interference and the adaptive Krylov subspace algorithm suppresses homogeneous clutter. The novel method uses a single snapshot of the data received by the array antenna to generate a cancellation matrix that does not contain the signal of interest (SOI) component, thus, it mitigates the problem of highly nonstationary clutter environment and it helps to operate in real-time. The benefit of not requiring the training data comes at the cost of a reduced degree of freedom (DOF) of the system. Simulation illustrates the effectiveness in clutter suppression and adaptive beamforming. The numeric results show good agreement with the proposed theorem.
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