Applications of probabilistic model based on joystick probability selector

Recently, it has been shown that a probabilistic model based on two of the main concepts in quantum physics - a density matrix and the Born rule, can be suitable for the modeling of learning algorithms in biologically plausible artificial neural networks framework. It has been shown that the proposed probabilistic interpretation is suitable for modeling on-line learning algorithms for Independent /Principal/Minor Component Analysis, which could be realized on parallel hardware based on very simple computational units. Also, it has been shown that the quantum entropy of the system, related to that model, can be successfully used in the problems like change point detection. Here, it will be shown that the proposed model can be successfully used in other areas of applied signal processing, with some examples of applications in the area of power electronics and general classification problems.