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**Multi-innovation least squares identification methods based on the auxiliary model for MISO systems.**
*(English)*
Zbl 1114.93101

Summary: For multi-input, single-output output-error systems, a difficulty in identification is that the information vector in the identification model obtained contains unknown inner/intermediate variables; thus the standard least squares methods cannot be applied directly. In this paper, we present a multi-innovation least squares identification algorithm based on the auxiliary model; its basic idea is to replace the unknown inner variables with their estimates computed by an auxiliary model. Convergence analysis indicates that the parameter estimation error converges to zero under persistent excitation. The algorithm proposed has significant computational advantage over existing identification algorithms. A simulation example is included.

### MSC:

93E24 | Least squares and related methods for stochastic control systems |

93E12 | Identification in stochastic control theory |

93C35 | Multivariable systems, multidimensional control systems |

### Keywords:

recursive identification; estimation; least squares; multi-innovation identification; hierarchical identification; auxiliary model; multivariable systems; convergence properties
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\textit{F. Ding} et al., Appl. Math. Comput. 187, No. 2, 658--668 (2007; Zbl 1114.93101)

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### References:

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