Convergence Analysis of a GA-ICA Algorithm
In this work we consider the extension of Genetic-Independent Component Analysis Algorithms (GA-ICA) with guiding operators and prove their convergence to the optimum. This novel method for Blindly Separating unobservable independent component Sources (BSS) consists of novel guiding genetic operators (GGA) and finds the separation matrix which minimizes a contrast function. The convergence is shown under little restrictive conditions for the guiding operator: its effect must disappear in time like the simulated annealing.
KeywordsMean Square Error Transition Probability Matrix Time Series Forecast Contrast Function Stochastic Matrice
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