Another disadvantage of the SER and RLS are their slow computation speed. Depending on the speed of the computer, the user will see a significant change when these methods are chosen. The project will also allow the users to choose three types of simulation. The user can choose a type of system identification, noise cancellation, or prediction. The desired signal is the input signal, s , and the delayed version of the latter is sent to the adaptive processor, which therefore attempt to predict the current input signal in order to have y cancel d and drive e toward zero.
Prediction is used in signal encoding and noise reduction.
Here the signal, s , is the input to the adaptive processor as well as to the unknown plant. To reduce e , the adaptive processor tries to emulate the plant's characteristic. Here the signal, s , interfere by the additive noise, n , and distorted, but the correlated version of the noise, n' , is also available. The goal of the adaptive processor in this case is to produce an output, y , that closely resembles n , so that the adaptive processor will minimize the error, e . Some of the major problems that I encountered while working on the project was figuring out how to compute linear algebra in java, import an existing package, and coding the adaptive algorithms.