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Higher Order Statistical Techniques Analysis for Non-linear Systems Evaluation

Saurabh Kumar Pandey, Chanchal Soni, and Anjali Gupta
Electronics & Communication Department, S.V.I.T.S, Indore, India
Abstract—The present paper deals with the analysis of non linear system using higher order statistical techniques namely Independent component analysis (ICA). In reality, the data often does not follow a Gaussian distribution and the situation is not as simple as those methods of factor analysis, projection pursuit or PCA assumes. Many real world data sets have super Gaussian Distributions. Hence the probability density of the data is peaked at zero and has many tails, when compared to a Gaussian density of the same variance. This is the starting point of ICA, where we try to find statistically independent components in the general case where the data is non Gaussian. In this paper we had presented the different estimation principles of ICA and their algorithms. The simulation results of ICA were carried out by MATLAB.

Index Terms—ICA, nonlinear system, non-gaussianity, statistical independence

Cite: Saurabh Kumar Pandey, Chanchal Soni, and Anjali Gupta, "Higher Order Statistical Techniques Analysis for Non-linear Systems Evaluation," International Journal of Electronics and Electrical Engineering, Vol. 3, No. 1, pp. 24-27, February 2015. doi: 10.12720/ijeee.3.1.24-27
 
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