Abstract

A self-adjusting method for calculating recurrence diagrams has been developed. The proposed method is aimed at overcoming the metric-threshold uncertainty inherent in the known methods for calculating recurrence diagrams. The method provides invariance to the nature of the measured data, and also allows to display the recurrence of states, adequate to real systems of various fields. A new scientific result consists in the theoretical justification of the method for calculating recurrence diagrams, which is capable of overcoming the existing metric-threshold uncertainty of known methods on the basis of self-adjusting by measurements by improving the topology of the metric space. The topology is improved due to the additional introduction of the scalar product of state vectors into the operation space. This allowed to develop a self-adjusting method for calculating recurrence diagrams with increased accuracy and adequacy of the display of recurrence states of real systems. Moreover, the method has a relatively low computational complexity, providing invariance with respect to the nature of the irregularity of measurements. Verification of the proposed method was carried out on the basis of experimental measurements of concentrations of gas pollutants of atmospheric air for a typical industrial city. The main gas pollutants of the atmosphere are formaldehyde, ammonia and nitrogen dioxide, caused by stationary and mobile sources of urban pollution. The obtained experimental verification results confirm the increased accuracy and adequacy of the display of the recurrence of atmospheric pollution states, as well as the invariance of the method with respect to the nature of the irregularity of measurements. It has been established that the accuracy of the method is influenced by the a priori boundary angular dimensions of the recurrence cone. It was shown that with a decrease in the boundary angular dimensions of the recurrence cone, the accuracy of the recurrence mapping of the real states of dynamical systems in the calculated diagrams increases. It was experimentally established that the accuracy and adequacy of the mapping of the recurrence states of real dynamical systems acceptable for applications is provided for a boundary angular size of the recurrence cone of 10° or less.

Highlights

  • The methods for calculating recurrence diagrams (RP) and quantitative assessment of recurrence diagrams (RQA) allow to analyze the behavior of complex dynamic systems of various fields [1]

  • (2019), «EUREKA: Physics and Engineering» Number 5 aimed at overcoming metric-threshold uncertainty in order to obtain adequate mappings of recurrent states of real systems

  • A self-adjusting measurement method for calculating RP has been developed, which allows overcoming the existing metric-threshold uncertainty of known methods. This ensures the invariance of the method to the nature of the measured data and allows to display the recurrence of states on the diagrams, adequate to real dynamic systems

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Summary

Introduction

The methods for calculating recurrence diagrams (RP) and quantitative assessment of recurrence diagrams (RQA) allow to analyze the behavior of complex dynamic systems of various fields [1]. Well-known heuristics are of a private nature, limited to well-known metrics and have significant implementation difficulties and shortcomings In this regard, an important and unresolved part of the problem is the development of a method for calculating RP (2019), «EUREKA: Physics and Engineering» Number 5 aimed at overcoming metric-threshold uncertainty in order to obtain adequate mappings of recurrent states of real systems. Within the framework of the development of the developed paradigm, it is advisable to move from considering the norms and generating metrics that define the corresponding spaces traditional for the methods of computing RP functionals to the corresponding improved space [2, 3] Such an improvement in [3] is proposed to be implemented by introducing an additional geometric characteristic in the form of a scalar product of two vectors. The characteristic function (2) in the improved space can be represented as

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