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When a conventional NLMS adaptive filter is used to predict a process, especially when predicting several samples ahead, non-linear effects can be observed. These non-linear effects produce adaptive filter performance that exceeds that of the conventional Wiener filter, and engenders weight behavior that is of a time-varying nature. After showing the existence of such non-linear effects, we show their relation to the difference between the structure of the optimal predictor and the structure used to model the data to be predicted. The non-linear effects are strong when the process to be predicted is more narrowband.

• Non-Linear Effects
• NLMS
• Time-Varying Wiener Filter
• Multi-Channel Wiener Filter
• Multi-Channel Adaptive Filter
• Adaptive Prediction