Analysis of quasi-periodic space-time non-separable processes to support decision-making in medical monitoring systems

Abstract

In many decision support systems there are processed chaotic spatial-time processes which are non-separable and quasiperiodic. Some examples of such systems are epidemic spreading, population development, fire spreading, radio wave signals, image processing, information encryption, radio vision, etc. Processes in these systems have periodic character, e.g. seasonal fluctuations (epidemic spreading, population development), harmonic fluctuations (pattern recognition, image processing), etc. In simulation block the existing systems use separable process models which are presented as multiplication of spatial and temporal parts and are linearized. This significantly reduces the quality of spatial-time non-separable processes. The quality model building of chaotic spatial-time non-separable process which is processed by decision support system is necessary for getting of learning set. It is really complicated especially if the random process is formed. The implementation ensemble of chaotic spatial-time non-separable process requires high costs what causes reduction of the system efficiency. Moreover, in many cases the implementation ensemble of spatialtime processes is impossible to get. In this work the mathematical model of a quasi-periodic spatial-time non-separable process has been developed. Based on it the formation method of this process has been developed and investigated. The epidemic spreading processed was presented as an example.