Adaptive RLS algorithms under stochastic excitation-L/sup 2/ convergence analysis

Bittanti S.; Campi M.

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Adaptive RLS algorithms under stochastic excitation-L/sup 2/ convergence analysis

机译：随机激励-L / sup 2 /收敛性分析下的自适应RLS算法

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A very general class of RLS (recursive least squares) algorithms having a forgetting factor is considered. The basic assumptions are that the data generation mechanism is free of disturbances and that the observation vector is a stochastic process satisfying a phi -mixing condition. A stochastic characterization of persistent excitation is first given. Then, it is proved that the algorithm is exponentially convergent in the mean-square sense.

机译：考虑了具有遗忘因子的非常普通的RLS（递归最小二乘）算法类。基本假设是数据生成机制没有干扰，并且观测向量是满足phi混合条件的随机过程。首先给出了持久激励的随机特征。然后，证明了该算法在均方意义上是指数收敛的。

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《IEEE Transactions on Automatic Control》 |1991年第8期|P.963-967|共5页
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Bittanti S.; Campi M.;
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Adaptive RLS algorithms under stochastic excitation-L/sup 2/ convergence analysis

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