This paper gives a robustness analysis of the stochastic approximation algorithms in the situations: when the regression function does not exactly equal zero at the sought-for χo, when the Liapunov function is not ze...
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This paper gives a robustness analysis of the stochastic approximation algorithms in the situations: when the regression function does not exactly equal zero at the sought-for χo, when the Liapunov function is not zero at χoand when anΣni=1X;i+1differs from zero where {aig} are the weighting coefficients of the algorithm and {Xi} are the measurement errors. It is shown that the estimation error is small if the abovementioned differences are small.
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