文件名称:LS
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the linear ARX model as shown below [12] is used to represent the input and the output data for the system. This ARX model is utilized for both Least Square (LS) and Recursive Least Square (RLS) algorithms. It is one of the simplest statistical methods for system identification used to find the transfer function for the model. The equations given by [15]:
Y=φβ+ξ (7)
β=〖(φ^T φ)〗^(-1) φ^T Y (8)
where Y is the predicted output. φ is includes actual input and output parameters. β is includes parameters to be estimated.
-the linear ARX model as shown below [12] is used to represent the input and the output data for the system. This ARX model is utilized for both Least Square (LS) and Recursive Least Square (RLS) algorithms. It is one of the simplest statistical methods for system identification used to find the transfer function for the model. The equations given by [15]:
Y=φβ+ξ (7)
β=〖(φ^T φ)〗^(-1) φ^T Y (8)
where Y is the predicted output. φ is includes actual input and output parameters. β is includes parameters to be estimated.
Y=φβ+ξ (7)
β=〖(φ^T φ)〗^(-1) φ^T Y (8)
where Y is the predicted output. φ is includes actual input and output parameters. β is includes parameters to be estimated.
-the linear ARX model as shown below [12] is used to represent the input and the output data for the system. This ARX model is utilized for both Least Square (LS) and Recursive Least Square (RLS) algorithms. It is one of the simplest statistical methods for system identification used to find the transfer function for the model. The equations given by [15]:
Y=φβ+ξ (7)
β=〖(φ^T φ)〗^(-1) φ^T Y (8)
where Y is the predicted output. φ is includes actual input and output parameters. β is includes parameters to be estimated.
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