基于优化G-P算法求解关联维数OACSTPCD
Calculation of Correlation Dimension Based on Optimized G-P Algorithm
为了解决研究混沌关联维数主流算法G-P算法存在的问题,包括相空间和距离矩阵的循环构造,相关参量选取不当影响结论,Heaviside阶跃函数判断冗余等,提出改进的G-P算法.该算法在原理和重复计算上作出优化,在原矩阵的基础上操作形成后续矩阵及对距离矩阵进行排序,避免了循环判断,大大缩减了计算周期;对相关参量取值做出决策,使结论更严谨;对双对数图像的无标度区间采用Taylor级数法确定,使其更加直观.后续以Lorenz系统数据进行实例分析,仿真的结果也证明了改进算法的准确性.
In order to solve problems in the study of the mainstream algorithm G-P algorithm of chaotic associated dimension,including the cycle structure of the phase space and distance matrix,the relevant parameter selection is not properly affected,and the Heaviside step function determines redundant,etc.,an improved G-P algorithm is proposed.The algorithm is optimized in prin-ciple and repetition calculation,and operates a subsequent matrix on the basis of the original matrix and sorts the distance matrix to avoid cyclic judgment,which greatly reduces the calculation cycle.It makes decisions to the related parameters,and makes the con-clusion strictly.The non-scaling section of the double logarithmic image is determined by the Taylor grade method,making it more intuitive.Subsequently,the system data is used for case analysis,the results of simulation also prove the accuracy of the improved algorithm.
方若望;何越磊;李再帏;路宏遥;赵彦旭
上海工程技术大学城市轨道交通学院 上海 201620中铁二十一局集团有限公司 兰州 730070
计算机与自动化
混沌相空间重构关联维数算法优化G-P算法
chaosphase space reconstructioncorrelation dimensionalgorithmic optimizationG-P algorithm
《计算机与数字工程》 2024 (005)
1270-1274 / 5
国家自然科学基金项目(编号:51978393);甘肃省科技计划项目(编号:19ZD2FA001);中国铁建科技研发计划项目(编号:2019-B08)资助.
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