11. Adaptive Filters: Prediction and System Identification
Adaptive filters adjust their parameters based on input signals, making them invaluable for applications such as prediction and system identification. The chapter emphasizes the LMS algorithm, detailing its functioning and significance in minimizing error in real-time filtering. Further discussions delve into practical implementations like noise cancellation, demonstrating the adaptive filter's capability to enhance signal quality across various dynamic environments.
Sections
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What we have learnt
- Adaptive filters are essential for dynamically changing environments where signal characteristics vary.
- The LMS algorithm is widely used for adaptive filtering, minimizing mean square error through iterative coefficient updates.
- Adaptive filters serve numerous applications, including prediction, noise cancellation, and system identification.
Key Concepts
- -- Adaptive Filters
- Filters that adjust their parameters automatically based on the input signal to optimize performance in real-time.
- -- LMS Algorithm
- An adaptive filtering algorithm that updates filter coefficients to minimize the mean square error between desired and actual outputs.
- -- Prediction
- The process of estimating future values based on past observations using adaptive filtering techniques.
- -- System Identification
- The process of estimating parameters of an unknown system by analyzing its input-output behavior using adaptive filters.
Additional Learning Materials
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