Independent Component Analysis. ICA Neural Network. Java implementation

Independent component analysis (ICA) aims to solve problem of signals separation from their linear mixture. ICA is a special case of blind source separation, when separation performed without the aid of information (or with very little information) about the source signals or the process of signal mixing.  Although blind source separation problem in general is underdetermined, the useful solution can be obtained under a certain assumptions.

ICA model assumes that there are  independent signals  and some mixing matrix  :

FIR filter programming and testing

About FIR (finite impulse response) filter programming in C I have written in my old post. Now I want to show the object-oriented programming approach to solve this task using Java. 

Also there is some improvements: the cyclic buffer is used to convolve filter kernel with  input data samples. It reduces the number of calculations, because there is no need to shift all input data through array where data stored. You just have to put input sample in write place and shift array index (or "pointer" to element).

FIR filter implementation

FIR (finite impulse response) filter is a digital filter without feedback. Absence of feedback makes impulse filter response to be finite. Digital filters can be with infinite impulse response (IIR filters).
The advantages of FIR filters compared to IIR filters:
1. FIR filter can be designed with linear phase.
2. FIR filter is simple to implement.