Adaptive filter
An adaptive filter is a system with a linear filter that has a transfer function controlled by variable parameters and a means to adjust those parameters according to an optimization algorithm. Because of the complexity of the optimization algorithms, almost all adaptive filters are digital filters. Adaptive filters are required for some applications because some parameters of the desired processing operation (for instance, the locations of reflective surfaces in a reverberant space) are not known in advance or are changing. The closed loop adaptive filter uses feedback in the form of an error signal to refine its transfer  function. Generally speaking, the closed loop adaptive process involves the use of a cost function, which is a criterion for optimum performance of the filter, to feed an algorithm, which determines how to modify filter transfer function to minimize the cost on the next iteration. The most common cost function is the mean square of the error signal. As the power of digital signal processors has increased, adaptive filters have become much more common and are now routinely used in devices such as mobile phones and other communication devices, camcorders and digital cameras, and medical monitoring equipment. Example application. The  recording of a heart beat (an ECG), may be corrupted by noise from the AC mains. The exact frequency of the power and its harmonics may vary from moment to moment. One way to remove the noise is to filter the signal with a notch filter at the mains frequency and its vicinity, but this could excessively degrade the quality of the ECG since the heart beat would also likely have frequency components in the rejected range. To circumvent this potential loss of information, an adaptive filter could be used. The adaptive filter would take input both from the patient  and from the mains and would thus be able to track the actual frequency of the noise as it fluctuates and subtract the noise from the recording. Such an adaptive technique generally allows for a filter with a smaller rejection range, which means, in this case, that the quality of the output signal is more accurate for medical purposes. Block diagram. The idea behind a closed loop adaptive filter is that a variable filter is adjusted until the error (the difference between the filter output and the desired signal) is minimized. The Least Mean Squares (LMS) filter and the Recursive  Least Squares (RLS) filter are types of adaptive filter. There are two input signals to the adaptive filter: formula_1 and formula_2 which are sometimes called the "primary input" and the "reference input" respectively. The adaptation algorithm attempts to filter the reference input into a replica of the desired input by minimizing the residual signal, formula_3. When the adaptation is successful, the output of the filter formula_4 is effectively an estimate of the desired signal. The filter is controlled by a set of L+1 coefficients or weights. The output is usually formula_3 but it could be formula_4 or it could even  be the filter coefficients.(Widrow) The input signals are defined as follows: The output signals are defined as follows: Tapped delay line FIR filter. If the variable filter has a tapped delay line Finite Impulse Response (FIR) structure, then the impulse response is equal to the filter coefficients. The output of the filter is given by Ideal case. In the ideal case formula_24. All the undesired signals in formula_1 are represented by formula_26. formula_27 consists entirely of a signal correlated with the undesired signal in formula_26. The output of the variable filter in the ideal case is The error signal or  cost function is the difference between formula_1 and formula_4 The error signal formula_3 is minimized in the mean square sense when formula_34 is minimized. In other words, formula_35 is the best mean square estimate of formula_26. In the ideal case, formula_37 and formula_38, and all that is left after the subtraction is formula_39 which is the unchanged desired signal with all undesired signals removed. Signal components in the reference input. In some situations, the reference input formula_40 includes components of the desired signal. This means g' ≠ 0. Perfect cancelation of the undesired interference is not possible in the case , but improvement of the signal to interference ratio is possible. The output will be The output signal to interference ratio has a simple formula referred to as "power inversion". This formula means that the output signal to interference ratio at a particular frequency is the reciprocal of the reference signal to interference ratio. Example: A fast food restaurant has a drive-up window. Before getting to the window, customers place their order by speaking into a microphone. The microphone also picks up noise from the engine and the environment. This microphone provides the primary signal. The signal power from the  customer's voice and the noise power from the engine are equal. It is difficult for the employees in the restaurant to understand the customer. To reduce the amount of interference in the primary microphone, a second microphone is located where it is intended to pick up sounds from the engine. It also picks up the customer's voice. This microphone is the source of the reference signal. In this case, the engine noise is 50 times more powerful than the customer's voice. Once the canceler has converged, the primary signal to interference ratio will be improved from 1:1  to 50:1. Adaptive Linear Combiner. The adaptive linear combiner (ALC) resembles the adaptive tapped delay line FIR filter except that there is no assumed relationship between the X values. If the X values were from the outputs of a tapped delay line, then the combination of tapped delay line and ALC would comprise an adaptive filter. However, the X values could be the values of an array of pixels. Or they could be the outputs of multiple tapped delay lines. The ALC finds use as an adaptive beam former for arrays of hydrophones or antennas. LMS algorithm. If the variable  filter has a tapped delay line FIR structure, then the LMS update algorithm is especially simple. Typically, after each sample, the coefficients of the FIR filter are adjusted as follows:(Widrow) The LMS algorithm does not require that the X values have any particular relationship; therefore it can be used to adapt a linear combiner as well as an FIR filter. In this case the update formula is written as: The effect of the LMS algorithm is at each time, k, to make a small change in each weight. The direction of the change is such that it would decrease the  error if it had been applied at time k. The magnitude of the change in each weight depends on μ, the associated X value and the error at time k. The weights making the largest contribution to the output, formula_4, are changed the most. If the error is zero, then there should be no change in the weights. If the associated value of X is zero, then changing the weight makes no difference, so it is not changed. Convergence. μ controls how fast and how well the algorithm converges to the optimum filter coefficients. If μ is too large, the  algorithm will not converge. If μ is too small the algorithm converges slowly and may not be able to track changing conditions. If μ is large but not too large to prevent convergence, the algorithm reaches steady state rapidly but continuously overshoots the optimum weight vector. Sometimes, μ is made large at first for rapid convergence and then decreased to minimize overshoot. Widrow and Stearns state in 1985 that they have no knowledge of a proof that the LMS algorithm will converge in all cases. However under certain assumptions about stationarity and independence it can be shown that the algorithm  will converge if In the case of the tapped delay line filter, each input has the same RMS value because they are simply the same values delayed. In this case the total power is This leads to a normalized LMS algorithm: Nonlinear Adaptive Filters. The goal of nonlinear filters is to overcome limitation of linear models. There are some commonly used approaches: Volterra LMS, Kernel adaptive filter, Spline Adaptive Filter and Urysohn Adaptive Filter. Many authors include also Neural networks into this list. The general idea behind Volterra LMS and Kernel LMS is to replace data samples by different nonlinear  algebraic expressions. For Volterra LMS this expression is Volterra series. In Spline Adaptive Filter the model is a cascade of linear dynamic block and static non-linearity, which is approximated by splines. In Urysohn Adaptive Filter the linear terms in a model are replaced by piecewise linear functions which are identified from data samples.