A genetic algorithm for training recognizers of latent abnormal behavior of dynamic systems

We consider the problem of automatic construction of algorithms for recognition of abnormal behavior segments in phase trajectories of dynamic systems. The recognition algorithm is trained on a set of trajectories containing normal and abnormal behavior of the system. The exact position of segments corresponding to abnormal behavior in the trajectories of the training set is unknown. To construct recognition algorithm, we use axiomatic approach to abnormal behavior recognition. In this paper we propose a novel two-stage training algorithm which uses ideas of unsupervised learning and evolutonary computation. The results of experimental evaluation of the proposed algorithm and its variations on synthetic data show statistically significant increase in recognition quality for the recognizers constructed by the proposed algorithm compared to the existing training algorithm.