central limit theorem

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central limit theorem

n
(Statistics) statistics the fundamental result that the sum (or mean) of independent identically distributed random variables with finite variance approaches a normally distributed random variable as their number increases, whence in particular if enough samples are repeatedly drawn from any population, the sum of the sample values can be thought of, approximately, as an outcome from a normally distributed random variable
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References in periodicals archive ?
Reference [28] shows that, if each layer data conforms to Lyapunov condition, the sum of each layer data will be in accordance with the application conditions of the central limit theorem; that is, the sum of each layer data will obey the normal distribution.
A system is stable at the point x if there is a function continually derivable U(x) that satisfies Lyapunov conditions:
Control signal should be derived such that Lyapunov conditions are satisfied.