Gaussian adaptation (GA) is an evolutionary algorithm designed for the maximization of manufacturing yield due to statistical deviation of component values of signal processing systems. In short, GA is a stochastic adaptive process where a number of samples of an n-dimensional vector x[x = (x1, x2, ..., xn)] are taken from a multivariate Gaussian distribution, N(m − x), having mean m and moment matrix M. The samples are tested for fail or pass. T...
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Gaussian adaptation (GA) is an evolutionary algorithm designed for the maximization of manufacturing yield due to statistical deviation of component values of signal processing systems. In short, GA is a stochastic adaptive process where a number of samples of an n-dimensional vector x[x = (x1, x2, ..., xn)] are taken from a multivariate Gaussian distribution, N(m − x), having mean m and moment matrix M. The samples are tested for fail or pass. The first and second order moments of the Gaussian restricted to the pass samples are m* and M*.
The outcome of x as a pass sample is determined by a function s(x), 0
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