MATHEMATICAL FOUNDATION OF A NEW COMPLEXITY MEASURE
For many continuous bio-medieal signals with both strong nonlinearity and non-stationarity, two criterions were proposed for their complexity estimation ： （1） Only a short data set is enough for robust estimation; （2） No over-coarse graining preproeessing, such as transferring the original signal in...
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| Published in | Applied mathematics and mechanics Vol. 26; no. 9; pp. 1188 - 1196 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
School of Life Science, Research Center for Brain Science, Institute of Brain Science, Fudan University, Shanghai 200433, P. R. China%School of Mathematical Sciences, Research Center for Nonlinear Science, Fudan University, Shanghai 200433, P. R. China
01.09.2005
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0253-4827 1573-2754 |
| DOI | 10.1007/BF02507729 |
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| Abstract | For many continuous bio-medieal signals with both strong nonlinearity and non-stationarity, two criterions were proposed for their complexity estimation ： （1） Only a short data set is enough for robust estimation; （2） No over-coarse graining preproeessing, such as transferring the original signal into a binary time series, is needed. Co complexity measure proposed by us previously is one of such measures. However, it lacks the solid mathematical foundation and thus its use is limited. A modified version of this measure is proposed, and some important properties are proved rigorously. According to these properties, this measure can be considered as an index of randomness of time series in some senses, and thus also a quantitative index of complexity under the meaning of randomness finding complexity. Compared with other similar measures, this measure seems more suitable for estimating a large quantity of complexity measures for a given task, such as studying the dynamic variation of such measures in sliding windows of a long process, owing to its fast speed for estimation. |
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| AbstractList | For many continuous bio-medieal signals with both strong nonlinearity and non-stationarity, two criterions were proposed for their complexity estimation ： （1） Only a short data set is enough for robust estimation; （2） No over-coarse graining preproeessing, such as transferring the original signal into a binary time series, is needed. Co complexity measure proposed by us previously is one of such measures. However, it lacks the solid mathematical foundation and thus its use is limited. A modified version of this measure is proposed, and some important properties are proved rigorously. According to these properties, this measure can be considered as an index of randomness of time series in some senses, and thus also a quantitative index of complexity under the meaning of randomness finding complexity. Compared with other similar measures, this measure seems more suitable for estimating a large quantity of complexity measures for a given task, such as studying the dynamic variation of such measures in sliding windows of a long process, owing to its fast speed for estimation. TN911.72%TN911.73; For many continuous bio-medical signals with both strong nonlinearity and non-stationarity, two criterions were proposed for their complexity estimation: (1) Only a short data set is enough for robust estimation; (2) No over-coarse graining preprocessing, such as transferring the original signal into a binary time series, is needed. C0 complexity measure proposed by us previously is one of such measures.However, it lacks the solid mathematical foundation and thus its use is limited. A modified version of this measure is proposed, and some important properties are proved rigorously. According to these properties, this measure can be considered as an index of randomness of time series in some senses, and thus also a quantitative index of complexity under the meaning of randomness finding complexity. Compared with other similar measures, this measure seems more suitable for estimating a large quantity of complexity measures for a given task, such as studying the dynamic variation of such measures in sliding windows of a long process, owing to its fast speed for estimation. |
| Author | 沈恩华 蔡志杰 顾凡及 |
| AuthorAffiliation | School of Life Science, Research Center for Brain Science, Institute of Brain Science, Fudan University, Shanghai 200433, P. R. China School of Mathematical Sciences, Research Center for Nonlinear Science, Fudan University, Shanghai 200433, P. R. China |
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| Cites_doi | 10.1109/TIT.1976.1055501 10.1103/PhysRevLett.80.5019 10.1007/s004220000158 10.1073/pnas.88.6.2297 10.1142/S021812740401093X |
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| Title | MATHEMATICAL FOUNDATION OF A NEW COMPLEXITY MEASURE |
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