| Neural Volterra filter for chaotic time series prediction |
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| 引用本文： | 李恒超,张家树,肖先赐. Neural Volterra filter for chaotic time series prediction[J]. 中国物理, 2005, 14(11): 2181-2188 |
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| 作者姓名： | 李恒超 张家树 肖先赐 |
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| 作者单位： | Sichuan Province Key Laboratory of Signal and Information Processing,Southwest Jiaotong University, Chengdu 610031, China;Sichuan Province Key Laboratory of Signal and Information Processing,Southwest Jiaotong University, Chengdu 610031, China;Department of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China |
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| 基金项目： | Project supported by the National Natural Science Foundation of China (Grant No 60276096), the National Ministry Foundation of China (Grant No 51430804QT2201). |
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| 摘 要： | A new second-order neural Volterra filter （SONVF） with conjugate gradient （CG） algorithm is proposed to predict chaotic time series based on phase space delay-coordinate reconstruction of chaotic dynamics system in this paper, where the neuron activation functions are introduced to constraint Volterra series terms for improving the nonlinear approximation of second-order Volterra filter （SOVF）. The SONVF with CG algorithm improves the accuracy of prediction without increasing the computation complexity. Meanwhile, the difficulty of neuron number determination does not exist here. Experimental results show that the proposed filter can predict chaotic time series effectively, and one-step and multi-step prediction performances are obviously superior to those of SOVF, which demonstrate that the proposed SONVF is feasible and effective.
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| 关 键 词： | 无序时间序列 Volterra神经过滤 空间延迟 适应算法 |
| 收稿时间： | 2005-03-31 |
| 修稿时间： | 2005-03-312005-05-11 |
Neural Volterra filter for chaotic time series prediction |
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| Li Heng-Chao,Zhang Jia-Shu and Xiao Xian-Ci. Neural Volterra filter for chaotic time series prediction[J]. Chinese Physics, 2005, 14(11): 2181-2188 |
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| Authors: | Li Heng-Chao Zhang Jia-Shu Xiao Xian-Ci |
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| Affiliation: | Department of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China; Sichuan Province Key Laboratory of Signal and Information Processing,Southwest Jiaotong University, Chengdu 610031, China |
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| Abstract: | A new second-order neural Volterra filter (SONVF) with conjugate gradient (CG) algorithm is proposed to predict chaotic time series based on phase space delay-coordinate reconstruction of chaotic dynamics system in this paper, where the neuron activation functions are introduced to constraintVolterra series terms for improving the nonlinear approximation of second-order Volterra filter (SOVF). The SONVF with CG algorithm improves the accuracy of prediction without increasing the computation complexity. Meanwhile, the difficulty of neuron number determination does not exist here. Experimental results show that the proposed filter can predict chaotic time series effectively, and one-step and multi-step prediction performances are obviously superior to those of SOVF, which demonstrate that the proposed SONVF is feasible and effective. |
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| Keywords: | chaotic time series adaptive neural Volterra filter conjugate gradient algorithm |
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