Abstract
Minor component analysis (MCA) is an important feature extraction technique which has been widely applied in data analysis fields. MCA neural networks generally are used to extract online minor component in term of adapting the demands of real time and decreasing computational complexity. However, the MCA learning algorithm can produce complicated dynamical behavior under some conditions, such as the periodic oscillation, bifurcation and chaos. In this paper, the chaos control of Douglas's MCA is addressed, and the stability transformation method(STM) of chaos feedback control is utilized to the convergence control of Douglas's MCA. Time series diagrams, Lyapunov exponent of dynamical system demonstrate that the desired fixed points of iterative map of Douglas's MCA can be captured, and the chaotic behavior of the algorithm can be controlled in the original chaotic interval.
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