Informatica

The paper considers application of stochastic optimization to system of automatic recognition of ischemic stroke area on computed tomography (CT) images. The algorithm of recognition depends on five inputs that influence the results of automatic detection. The quality of recognition is measured by size of conjunction of ethalone image and the image calculated by the program of automatic detection. The method of Simultaneous Perturbation Stohastic Approximation algorithm with the Metropolis rule has been applied to the optimization of the quality of image recognition. The Monte-Carlo simulation experiment was performed in order to evaluate the properties of developed algorithm.

Methods for solving stochastic optimization problems by Monte-Carlo simulation are considered. The stoping and accuracy of the solutions is treated in a statistical manner, testing the hypothesis of optimality according to statistical criteria. A rule for adjusting the Monte-Carlo sample size is introduced to ensure the convergence and to find the solution of the stochastic optimization problem from acceptable volume of Monte-Carlo trials. The examples of application of the developed method to importance sampling and the Weber location problem are also considered.