Scope and method of study. A novel method for identification of steady state is demonstrated as the termination criterion for the optimization stage of modeling empirical data. The method was tested on a variety of applications. It is described, and its utility is demonstrated on modeling simulated data and is also validated using two laboratory scale experiments.; Findings and conclusions. The novel stopping criterion for optimization, based on identifying steady state of a random subset of the sum of squared deviations with respect to iteration number, was formerly explored for neural network training. The novel stop-optimization criterion was tested on a different variety of applications involving various kinds of objective functions. On all the cases, the novel stop-optimization criterion gives equivalent results (as measured by model residuals) to the best possible results, with a sufficient (not excessive) number of iterations and without a priori knowledge of the optimization problem (scale, end-point values, and other classic stopping criteria).
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