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This paper considers the depth control problem of autonomous underwater vehicles (AUVs) in discrete time. A neural-network-based deterministic policy gradient (NNDPG) controller is proposed by combining the deterministic policy gradient theorem with neural networks. Two networks, evaluation network and policy network, are designed to respectively approximate the long-term cost function and policy...
With increasingly popular services containing asymmetric uplink (UL) and downlink (DL) traffic, Time-Division Duplexing (TDD) shows more advantages than Frequency-Division Duplexing (FDD) in wireless cellular networks due to its flexibility of dynamic UL/DL configuration. A major technical challenge in TDD is how to optimize the UL/DL switching point for each individual cell to meet its asymmetric...
An efficient implementation of generalized predictive control using multi-layer feed forward neural network as the plantpsilas nonlinear model is presented. Two algorithm i.e. Newton Raphson and Levenberg Marquardt algorithm are implemented and their results are compared. The details about this implementation are given. The utility of each algorithm is outlined in the conclusion. In using Levenberg...
Blind equalization based on compensation fuzzy neural network has proposed. It using compensation neurons to get tradeoff between negative and active operation, thus the network can be training using fuzzy rule of initial right or wrong setting, and fault tolerance and stability are improved. Simulations results show that this algorithm has faster convergence speed and less residual error compared...
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