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Description: Matlab系列。矩阵计算各类算法比较分析。-Matlab series. Matrix comparative analysis of the various algorithms.
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Size: 363520 |
Author: 夏泽洋 |
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Description: 关于matlab矩阵操作的初级教程，对新生很有用。建议大家看看！-Matlab matrix operation on the primary curriculum for freshmen useful. Recommendations to see everyone!
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Size: 191488 |
Author: feiyang |
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Description: Matlab code for the solution to Riccati matrix difference equations associated with the Kalman filter
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Size: 1024 |
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Description:
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Size: 13312 |
Author: 张治国 |
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Description: 通过邻接矩阵，计算网络的聚类系数。聚类系数是复杂网络中一个重要参量。
-Through the adjacency matrix, computing networks, clustering coefficient. Clustering coefficient is a complex network, an important parameter.
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Size: 1024 |
Author: tigercan |
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Description: This MATLAB code generates a square circulant matrix using the vector V as the first row of the result if V is a row vector or as the first column of the result if V is a column vector. V may be any numeric data type or a character string.
-This MATLAB code generates a square circulant matrix using the vector V as the first row of the result if V is a row vector or as the first column of the result if V is a column vector. V may be any numeric data type or a character string.
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Size: 3072 |
Author: Mohammed |
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Description: 嵌入维数自适应最小二乘支持向量机
状态时间序列预测方法
Condition Time Series Prediction Using Least Squares Support Vector Machine
with Adaptive Embedding Dimension
针对航空发动机状态时间序列预测中嵌入维数难于有效选取的问题, 提出一种基于嵌入维数自适应
最小二乘支持向量机( L SSVM ) 的预测方法。该方法将嵌入维数作为影响状态时间序列预测精度的重要参
数, 以交叉验证误差为评价准则, 利用粒子群优化( P SO ) 进化搜索LSSV M 预测模型的最优超参数与嵌入维
数, 同时通过矩阵变换原理提高交叉验证过程的计算效率, 并最终建立优化后的L SSVM 预测模型。航空发
动机排气温度( EGT ) 预测实例表明, 该方法可自适应选取适用于状态时间序列预测的最优嵌入维数且预测
精度高, 适用于航空发动机状态时间序列预测。- T o deal wit h the difficulty of selecting an appro pr iate embedding dimension for aeroeng ine co ndition
time series predictio n, a metho d based o n least squar es suppo rt vecto r machine ( L SSVM ) with ada ptive em
bedding dimension is pro po sed. I n the method, the embedding dimensio n is identified as a parameter that af
fects the accuracy o f the aer oengine condition time series predictio n par ticle sw arm o ptimizat ion ( P SO) is ap
plied to optimize the hyperpar ameter s and embedding dimension of the L SSV M pr edict ion model cro ssv alida
tion is applied to evaluate the perfo rmance o f the L SSVM predictio n mo del and matr ix tr ansfo rm is applied to
the L SSVM pr ediction model tr aining to accelerate the crossvalidation evaluation pro cess. Ex periments on an
aeroengine ex haust g as t emperatur e ( EGT ) predictio n demonst rates that the metho d is hig hly effective in em
bedding dimension selection. In compar ison w ith co nv
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Size: 342016 |
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