混沌時(shí)間序列的Volterra自適應(yīng)預(yù)測濾波器定階
Determining rank of volterra adaptive filter of chaotic time series
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摘要: 由于Volterra自適應(yīng)濾波器的階數(shù)對預(yù)測性能有較大的影響,在實(shí)際預(yù)測中,如何確定Volterra自適應(yīng)濾波器的最優(yōu)階數(shù)就成為一個(gè)關(guān)鍵問題,該文運(yùn)用相空間重構(gòu)理論,推導(dǎo)出了Volterra自適應(yīng)濾波器的最優(yōu)階數(shù)等于混沌動(dòng)力系統(tǒng)的最小嵌入維數(shù),作者用六種混沌時(shí)間序列進(jìn)行實(shí)驗(yàn),結(jié)果表明這種定階方法在混沌時(shí)間序列Volterra自適應(yīng)預(yù)測中非常成功,該方法對噪聲影響的變化,表現(xiàn)出較好的魯棒性。
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關(guān)鍵詞:
- 混沌; Volterra級數(shù); 最小嵌入維數(shù); 概率; 預(yù)測
Abstract: As the rank of Volterra adaptive filter interferes with predictive performance, how to determine the optimal rank of Volterra adaptive filter becomes a key problem in practical prediction. Using theory of phase space reconstruction, this paper derives that the optimal rank of Volterra adaptive filter equals the lowest embedding dimension of chaotic dynamical systems. It is shown through some chaotic series experiments that this method is successful in Volterra adaptive predication and robust to the noise of different levels added to the chaotic time series. -
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