Huangqiong Chen

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In this paper, a modified method for landslide prediction is presented. This method is based on the back-propagation neural network (BPNN), and we use the combination of genetic algorithm and simulated annealing algorithm to optimize the weights and biases of the network. The improved BPNN modeling can work out the complex nonlinear relation by learning(More)
Landslide deformation prediction has significant practical value that can provide guidance for preventing the disaster and guarantee the safety of people’s life and property. In this paper, a method based on recurrent neural network (RNN) for landslide prediction is presented. Genetic algorithm is used to optimize the initial weights and biases of the(More)
In this paper, a modified method for landslide prediction is presented. This method is based on the back propagation neural network(BPNN), and we use the combination of genetic algorithm and simulated annealing algorithm to optimize the weights and biases of the network. The improved BPNN modeling can work out the complex nonlinear relation by learning(More)
This paper presents some theoretical results on the global exponential stability of recurrent neural networks with pure time-varying delays. It is shown that the recurrent neural network is globally exponentially stable, if the pure time-varying delays satisfy some limitations. In addition to providing new criteria for recurrent neural networks with pure(More)
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