mean-square deviation

mean-square deviation

[′mēn ′skwer dē·vē′ā·shən]
(statistics)
A measure of the extent to which a collection v1, v2, …, vn of numbers is unequal; it is given by the expression (1/ n)[(v1- v̄)2+ ⋯ + (vn - v̄)2], where v̄ is the mean of the numbers.
References in periodicals archive ?
005, the convergent function is the mean-square deviation between the estimated coordinate (formula 1) and the reality coordinate of the unknown nodes, and the termination criteria is that the mean-square deviation declines.
i]) is the reality coordinate of the unknown node, i = 1~n;, n is the amount of the unknown nodes; f is the mean-square deviation mentioned above.
Calculate the mean-square deviation of the distances between unknown nodes and their neighbor beacon nodes of the best individual in the first generation, set the mean-square deviation as the convergence function, and set the probability of performing crossover, probability of mutation and termination criteria.