Independent Component Analysis And Its Application Study In Blind Signal Separation

Recently, theories and methods of signal processing are obtained to develop quickly. In fact, signal processing is an important foundation stone for all kinds of subjects' developing. Independent component analysis (ICA) technology developing is a later theory or method in fields of signal processing, but it was important that it has rapidly become a part of constitution of signal processing fields, and its developing tends gradually to maturity and systematization.In this paper, the theory and application of independent component analysis has been studied. The paper consists of following parts:1. First, the paper reviews systematically the present research situation of independent component analysis in the world. The basic principles and concepts of independent component analysis and some algorithms are introduced.2. Second, a minimum mutual information algorithm based recurrent neural networks are given after introduced the basic theory about Blind Source Separation. The main principle, derivation has been presented.3. Finally, Application research of blind signal separation by using independent component analysis has been studied in this paper. The feasible independence rule, brim entropy and activation function have been established. The design of fuzzy neural networks has been introduced particularly. And the performance of the algorithm has been illustrated by computation simulation experiment. The result display the performance of this algorithm is good.