A nonlinear feedback term is introduced into the evaluation equation of weights of the backpropagation algorithm for neural network, the network becomes a chaotic one. For the purpose of that we can investigate how th...A nonlinear feedback term is introduced into the evaluation equation of weights of the backpropagation algorithm for neural network, the network becomes a chaotic one. For the purpose of that we can investigate how the different feedback terms affect the process of learning and forecasting, we use the model to forecast the nonlinear time series which is produced by Makey-Glass equation. By selecting the suitable feedback term, the system can escape from the local minima and converge to the global minimum or its approximate solutions, and the forecasting results are better than those of backpropagation algorithm.展开更多
A new nonlinear prediction technique is proposed by feedforward neural network, the learning algorithmfor network is a chaotic one. A timc-delay embedding is used to reconstruct the underlying attractor, the predictio...A new nonlinear prediction technique is proposed by feedforward neural network, the learning algorithmfor network is a chaotic one. A timc-delay embedding is used to reconstruct the underlying attractor, the predictionmodel is based on the time evolution of the topological neighboring in the phase space, the spatial neighbors are chosenby the rate of exponential divergence of close trajectory. The model is tested for the Mackey-Glass delay equation andLorentz equations, good results are obtained for the prediction.展开更多
文摘A nonlinear feedback term is introduced into the evaluation equation of weights of the backpropagation algorithm for neural network, the network becomes a chaotic one. For the purpose of that we can investigate how the different feedback terms affect the process of learning and forecasting, we use the model to forecast the nonlinear time series which is produced by Makey-Glass equation. By selecting the suitable feedback term, the system can escape from the local minima and converge to the global minimum or its approximate solutions, and the forecasting results are better than those of backpropagation algorithm.
文摘A new nonlinear prediction technique is proposed by feedforward neural network, the learning algorithmfor network is a chaotic one. A timc-delay embedding is used to reconstruct the underlying attractor, the predictionmodel is based on the time evolution of the topological neighboring in the phase space, the spatial neighbors are chosenby the rate of exponential divergence of close trajectory. The model is tested for the Mackey-Glass delay equation andLorentz equations, good results are obtained for the prediction.
