基于暂态混沌神经网络的低阶混沌时间序列预测

暂态混沌神经网络是一种基于Hopfield网络提出的混沌神经网络，具有收敛速度快、不易陷入局部极小等优点．许多低阶的混沌系统都可以展成二阶volterra级数，因此提出一种基于暂态混沌神经网络和volterra级数的低阶混沌时间序列预测方法．该方法利用暂态混沌神经网络计算系统的volterra级数系数，确定系统的动力学模型，从而实现混沌时间序列预测．利用Logistic模型对该方法进行测试，结果表明，预测相对误差小于0．5％，预测可达到较高的速度和精度．

Chaotic time series prediction based on transiently chaotic neural network

The transiently chaotic neural network(TCNN) is proposed based on the Hopfield neural network(HNN),but it can escape from local minima more efficiently than HNN.Since a lot of simple chaotic time series can be expanded as second-order volterra series,a new prediction method based on TCNN and Volterra series is proposed in this paper.The method employed a TCNN to compute the coefficients of Volterra series,and then to decide the system's model.By using this model,a chaotic time series prediction method is realized.A logistic map is used to test the proposed model.The results show that the relative error is lower than 0.5%.The test results show that not only the convergence speed but also the accuracy are very high.