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In this correspondence, a novel linear prediction based blind equalization algorithm for Single Input Single Output (SISO) IIR channels is proposed. The new algorithm is based on second-order statistics and it does not require channel order estimation. By oversampling the channel output, the SISO channel model is converted to a special Single Input Multiple Output (SIMO) model. Two forward linear predictors with consecutive prediction steps are applied to the subchannel outputs of the SIMO model. It is proved that the partial parameters of the SIMO model can be estimated from the difference between the prediction errors when the length of the predictors is sufficiently large. The sufficient filter length for achieving the optimal prediction is also derived. Based on the estimated parameters, both batch and adaptive Minimum-Mean-Square-Error (MMSE) equalizers are developed. The performance of the proposed equalizers is evaluated by computer simulations and compared with existing algorithms.