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ISSUE 181, article 6

DOI:https://doi.org/10.15407/kvt181.01.070

Kibern. vyčisl. teh., 2015, Issue 181, pp.

Mayorov O.Y.

Kharkiv Medical Academy of Postgraduate Education, Kharkiv, Ukraine

Fenchenko V.N.

B. Verkin Institute for Low Temperature Physics and Engineering of the National Academy of Sciences of Ukraine, Kharkiv

MULTIFRACTAL ANALYSIS IN THE STUDY OF BRAIN BIOELECTRIC ACTIVITY

Introduction. A summary electroencephalogram (EEG) is composed of superimposed slow waves. The EEG reflects sophisticated cortical-subcortical interactions and conceals activity of multiple neuronal systems; each of them is characterized by determined neurodynamics.
The purpose of work is to create a method of objective quantitative assessment of parameters of multifractal summary bioelectric activity (EEG); to study EEG multifractality in healthy volunteers, subjects in altered states of conscious and pathologic EEGs.
Results. For the qualitative estimation of the multifractality of the EEG signal, the use of multifractal spectrum width, which can serve as an indicator of altered and pathologic brain states, is proposed. The state of different brain areas can also be assessed according to the offset value of a singularity spectrum of the transposition between different states. Analysis of Hölder exponents can provide an exact diagnostic tool and allow substantial interpretation of different processes in the brain.

Keywords: EEG, summary brain bioelectric activity, multifractality, wavelet transform maximum modulus method, method of multifractal detrended fluctuation analysis, Hölder exponent.

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References

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3 Stanley H.E., Amaral L.A.N., Goldberger A.L., Havlin S., Ivanov P.Ch., Peng C.-K. Statistical physics and physiology: Monofractal and multifractal approaches. Physica A, 1999. vol. 270. pp. 309–324. https://doi.org/10.1016/S0378-4371(99)00230-7

4 Nunes A.LA., Ivanov P.C., Aoyagi N., Hidaka I., Tomono S., Goldberger A. L., Stanley H.E. and Yamamoto Y. Behavioral-Independent Features of Complex Heartbeat Dynamics. Phys. Rev. Lett., 2001, vol. 86, pp. 6026–6029. https://doi.org/10.1103/PhysRevLett.86.6026

5 Ivanov P.Ch., Nunes Amaral L.A., Goldberger A.L., Havlin Sh., Rosenblum M.G., Stanley H.E., Struzik Zbigniew R. From 1/f noise to multifractal cascades in heartbeat dynamics. Chaos, 2001, vol. 11 pp. 641–652. https://doi.org/10.1063/1.1395631

6 Marrone A., Polosa A. D., Scioscia G., Stramaglia S. and Zenzola A. Multiscale analysis of blood pressure signals. Phys. Rev. E, 1999, vol. 60. pp. 1088–1091. https://doi.org/10.1103/PhysRevE.60.1088

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10 Kantelhardt J.W., Zschiegner S.A., Bunde A., Havlin S., Koscielny-Bunde E., Stanley H.E. Multifractal detrended fluctuation analysis of non-stationary time series. Physica A, 2002, no. 316, pp. 87–114. https://doi.org/10.1016/S0378-4371(02)01383-3

11 Kantelhardt J.W., Koscielny-Bunde E., Rego H.H.A., Havlin S., Bunde A. Detecting long-range correlations with detrended fluctuation analysis. Physica A, 2001, no. 295, pp. 441–454.

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13 Pavlov A.P., Anischenko V.S. Multifractal analysis of complex signals. Successes of physical sciences, 2007, vol. 177, no. 8, pp. 859–876 (in Russian).

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16 Hentschel H.G.E., Procaccia I. The infinite number of generalized dimensions of fractals and strange attractors. Physica D, 1983, Nonlinear Phenomena, vol. 8, no. 3, pp. 435–444. https://doi.org/10.1016/0167-2789(83)90235-X

17 Grassberger P., Procaccia I. Characterization of Strange Attractors. Physical Review Letters, 1983, vol. 50, no. 5, pp. 346–349. https://doi.org/10.1103/PhysRevLett.50.346

18 Oswiecimka P., Kwapin J., Drozdz S. Wavelet versus detrended fluctuation analysis of multifractal structures. Physical Review E, 2006, Statistical, Nonlinear, and Soft Matter Physics, vol. 74, pp. 161–203.

19 Veneziano D., Moglen G.E., Bras R.L. Multifractal analysis: pitfalls of standard procedures and alternatives. Phys. Rev, E. 1995, vol. 52, pp.1387–1398. https://doi.org/10.1103/PhysRevE.52.1387

20 Olemskoy A.I. Synergetics of complex systems: Phenomenology and statistical theory. M.: Krasandz Publ., 2009. 384 p. (in Russian).

21 Kirichenko L.O. Comparative multifractal time series analysis by methods of detrending fluctuation analysis and maxima of modulus the wavelet transform. All-Ukrainian interdep. scientific – technical proceedings of ASM and automation devices. Kh.: Publ. KhNURE, 2011, Vol. 157. pp. 66–77 (in Russian).

22 Frish U., Parisi G. On the singularity structure of fully developed turbulence. In: Turbulence and Predictability in Geophysical Fluid Dynamics and Climate Dynamics. Proc. of the Intern. School of Physics “Enrico Fermi”, Course 88, Eds by M. Gil, R. Benzi, G. Parisi. Proc. Amsterdam. North-Holland, 1985, pp. 84–88.

Received 27.03.2015

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ISSUE 181, article 5

DOI:https://doi.org/10.15407/kvt181.01.056

Kibern. vyčisl. teh., 2015, Issue 181, pp.

Melnichuk S.V.

Space Research Institute National Academy of Sciences of Ukraine and State Space Agency of Ukraine

METHOD OF STRUCTURAL PARAMETRIC MULTIVARIABLE SYSTEM IDENTIFICATION USING FREQUENCY CHARACTERISTICS

Introduction. One of the important directions in the identification of linear systems are frequency domain methods. In recent decades a finite-frequency approach, focused on the use under bounded uncertainty has been developed. Within finite-frequency approach a method, that allows to construct models with reduced dimensionality has been proposed. The method includes a step of structural identification with regularization by model dimension. This method was used to identify single-input single-output (SISO) systems, so it could not be applied to systems with multiple input and multiple output (MIMO).
Purpose. In order to generalize the method it is proposed to identify SISO models of subsystems, that describes individual inputs and outputs, and then combine them. The main purpose of research is to develop an algorithm, that combine separate SISO models into one general MIMO model.
Results. Separate SISO models determined by their invariant properties. As simple combination of SISO models leads to a MIMO model of large dimension, and some invariant properties in different models may be similar, it makes sense to carry out unification by equating this invariants.
Possibility of association for different combinations of SISO models, that have the same eigenvalues were investigated. It is shown that by combining models additional dependencies between coefficients may be imposed. It is shown that if the dependency graph contains no cycles, then the union is possible. On the basis of this fact the synthesizing algorithm was proposed.
Conclusions. The proposed identification algorithm builds the general MIMO model from separate SISO models so that the dimension of resulting model may be significantly less, than sum of dimensions of original SISO models. The proposed algorithm saves all invariant characteristics of the original models, so approximation accuracy by the each input-output relation is stored.

Keywords: System identification, frequency domain, structural identification, reduced dimensionality.

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References

1 Orlov Y.F. Frequency Parameter-Based Identification at Parallel Testing. Automation and Remote Control, 2007, vol.68, no. 1. pp. 18–37.

2 Orlov Y.F. Frequency Parameter-Based Identification at Parallel Testing. Avtomatika i Telemekhanika, 2007, vol.68, no. 1. pp. 20–40 (in Russian).

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5 Gubarev V.F., Melnychuk S.V. Identification of Multivariable Systems Using Steady-State Parameters. Journal of Automation and Information Sciences, 2012, vol. 44. i. 9. pp. 24–42.

6 Gubarev V.F., Melnychuk S.V. Identification of Multivariable Systems Using Steady-State Parameters. Journal of Automation and Information Sciences, 2012, no 5. pp. 26–42 https://doi.org/10.1615/JAutomatInfScien.v44.i9.30 (in Russian).

7 Melnychuk S.V. Regularity Investigation For Multidimensional System Identification Problem by the Frequency Method. Cybernetics and Computer Engineering, 2014, no 176. pp. 19–33 (in Russian).

8 Melnychuk S.V. Modified Frequency Method of Structural-Parametric System Identification. Journal of Automation and Information Sciences, 2015, no 4. pp. 27–36 (in Russian). https://doi.org/10.1615/JAutomatInfScien.v47.i8.60

Received 05.06.2015

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ISSUE 181, article 4

DOI:https://doi.org/10.15407/kvt181.01.043

Kibern. vyčisl. teh., 2015, Issue 181, pp.

Zhiteckii L.S., Nikolaienko S.A., Solovchuk K.Yu.

International Research and Training Center for Information Technologies and Systems of the National Academy of Science of Ukraine and Ministry of Education and Sciences of Ukraine, Kiev, Ukraine

ADAPTATION AND LEARNING IN SOME CLASSES OF IDENTIFICATION AND CONTROL SYSTEMS

Introduction. The paper deals with studying the asymptotical properties of the standard discrete-time gradient online learning algorithm in the two-layer neural network model of the uncertain nonlinear system to be identified. Also, the design of the discrete-time adaptive closed-loop system containing the linear multivariable memoryless plant with possibly singular but unknown matrix gain in the presence of unmeasurable bounded disturbances having the unknown bounds are addressed in this paper. It is assumed that the learning process in the neural network model is implemented in the stochastic environment whereas the adaptation of the plant model in the control system is based on the non-stochastic description of the external environment.
The purpose of the paper is to establish the global convergence conditions of the gradient online learning algorithm in the neural network model by utilizing the probabilistic asymptotic analysis and to derive the convergent adaptive control algorithm guaranteeing the boundedness of the signals in the closed-loop system which contains the multivariable memoryless plant with an arbitrary matrix gain in the presence of unmeasurable disturbances whose bounds are unknown.
Results. The Lyapunov function approach as the suitable tool for analyzing the asymptotic behavior both of the gradient learning algorithm in the neural network identification systems and of the adaptive gradient algorithm in the certain closed-loop control systems is utilized. Within this approach, the two groups of global sufficient conditions guaranteeing the convergence of the online gradient learning algorithm in neural network model with probability 1 are obtained. The first group of these conditions defines the requirements under which this algorithm will be convergent almost sure with a constant learning rate. Such an asymptotic property holds in the ideal case where the nonlinearity to be identified can exactly be described by a neural network model. The second group of convergence conditions shows that this property can also be achieved in non-ideal case. It turns out that adding a penalty term to the current error function is indeed not necessary to guarantee this property. It is established that in a worst case where the matrix gain of multivariable plant is unknown and may be singular, and the bounds on the arbitrary unmeasurable disturbances remain unknown, the convergence of the gradient adaptation algorithm and the boundedness of all signals in the adaptive closed-loop system can be ensured.
Conclusions. In order to guarantee the global convergence of the online learning algorithm in the neural network identification system with probability 1, the certain conditions should be satisfied. Also the boundedness of all signals in the closed-loop adaptive control system containing the multivariable memoryless plant whose matrix gain is unknown and possibly singular can be achieved even if the bounds on the unmeasurable disturbances are unknown.

Keywords: neural network, gradient learning algorithm, convergence, multivariable memoryless plant, adaptive control algorithm, boundedness of the signals.

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References

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3 Kuntsevich V.M. Control under Uncertainty Conditions: Guaranteed Results in Control and Identification Problems. Kiev: Nauk. dumka, 2006. (in Russian).

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7 Levin A.U. and Narendra K.S. Recursive identification using feedforward neural networks. Int. J. Control, 1995, vol. 61, pp. 533–547. https://doi.org/10.1080/00207179508921916

8 Tsypkin Ya.Z., Mason J.D., Avedyan E.D., Warwick K. and Levin I.K. Neural networks for identification of nonlinear systems under random piecewise polynomial disturbances. IEEE Trans. on Neural Networks, 1999, vol. 10, pp. 303–311. https://doi.org/10.1109/72.750559

9 Behera L., Kumar S., and Patnaik A. On adaptive learning rate that guarantees convergence in feedforward networks. IEEE Trans. on Neural Networks, 2006, vol. 17, pp. 1116–1125. https://doi.org/10.1109/TNN.2006.878121

10 White H. Some asymptotic results for learning in single hidden-layer neural network models. J. Amer. Statist. Assoc., 1987, vol. 84, pp. 117–134.

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12 Luo Z. On the convergence of the LMS algorithm with adaptive learning rate for linear feedforward networks. Neural Comput., 1991, vol. 3, pp. 226–245. https://doi.org/10.1162/neco.1991.3.2.226

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17 Zhang H., Wu W., Liu F. and Yao M. Boundedness and convergence of online gradient method with penalty for feedforward neural networks. IEEE Trans. on Neural Networks, 2009, vol. 20, pp. 1050–1054. https://doi.org/10.1109/TNN.2009.2020848

18 Mangasarian O.L. and Solodov M.V. Serial and parallel backpropagation convergence via nonmonotone perturbed minimization. Optim. Methods Software, 1994, pp. 103–106. https://doi.org/10.1080/10556789408805581

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20 Zhang N., Wu W. and Zheng G. Convergence of gradient method with momentum for two-layer feedforward neural networks. IEEE Trans. on Neural Networks, 2006, vol. 17, pp. 522–525. https://doi.org/10.1109/TNN.2005.863460

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22 Xu Z.B., Zhang R. and Jing W.F. When does online BP training converge? IEEE Trans. on Neural Networks, 2009, vol. 20, pp. 1529–1539. https://doi.org/10.1109/TNN.2009.2025946

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Received 06.07.2015

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ISSUE 181, article 3

DOI:https://doi.org/10.15407/kvt181.01.035

Kibern. vyčisl. teh., 2015, Issue 181, pp.

Hryshchenko Y.V., Skrypets A.V., Tronko V.D.

National Aviation University of Ministry of Education and Science of Ukraine (Kiev)

ANALYSIS OF THE CORRELATION FUNCTION OF THE GLIDESLOPE LANDING PATHS TAKING INTO ACCOUNT HUMAN FACTOR

Introduction. Nowadays,  proportion of accidents caused by the human factor (HF) is 80-90%. Despite the fact that these events are unlikely to happen, due to them at one time hundreds of lives may be taken away. Previous research works at assessment of change of integral-differential motor dynamic stereotype (IDDS) of pilot showed that the negative impact on the crew of factor overlays (FO) is simultaneously operating factors that often are imitated by failures in complex aircraft simulator significantly affect the quality of piloting technique (QPT). Stress caused by FO also leads to faulty actions. Simulation of FO action by implementation of complex failures on the simulator opens great opportunities for antistress training of pilots and the rest of the crew. Research has shown that in training certification centers approximately 70% of the pilots have no resistance to FO and the same pilots show the phenomenon of amplification of IDDS (PAIDDS), which is negative that there is an increase in the amplitude of the control motions that the operator does not notice without special training and equipment. It should be noted that the approach phase is the most accidental [1-3].
Motion Path of aircraft allows to determine the level of operator’s training, its psycho-physiological condition and quality of all elements of the aircraft. More common problem statement is how to define the technical and psychological state of the operator-machine-environment (SOME) at approaching the landing.
The purpose of this work is to determine opportunities and formation of mathematical models of the aircraft control by correlation functions while entering the glide path to improve the quality of landing.
Conclusions. In this study there is established that is possible to control the trajectory of the aircraft on glide path by the correlation function by our proposed formulas, especially: description of the correlation function without influence of factor overlaps and on the glide path with periodic factor overlap.
There are considered various options entering the glide path of an aircraft. We derive the numerical values of influence of timeliness of entrance to the glidepath on the quality of the landing at the outstrip and delay and it is shown that the correlation function of delay on outstrip is decreasing faster. It is concluded that the delay in entering the glide path by the pilot is more dangerous than outstrip.
The obtained results require the implementation in production technology of flight to improve the quality of trajectory control of the aircraft movement.

Keywords: correlation function, the human factor, glissade, flying.

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References

  1. Hryshchenko Yu.V. Amplification phenomenon of the dynamic stereotype of pilot under the influence of complex failures // Ergonomic flight safety issues: Collection of scientific tracts. — K.: KCAEI,К. 1987. — P. 87–91 (in Russian).
  2. Hryshchenko Yu.V. Analysis of changes in the dynamic stereotype of pilots in flight training on an integrated simulator of aircraft — Cybernetics and computer engineering: Interdepartmental collection of scientific papers. — K.: Publishing house “Academperіodics” NAS of Ukraine, 2004. — Edition. 142. — P. 35–40 (in Russian).
  3. Hryshchenko Y.V. Mathematical Description Amplification Phenomenon of Integral-Differential Motive Dynamic Stereotype // Methods and Systems of Navigation and Motion Control // Y.V. Hryshchenko, A. V. Skripets, V. D. Tronko / IEEE 3rd International Conference (October 14–17, 2014) — Kyiv, Ukraine, pp. 71–74.
  4. Norbert Wiener. Cybernetics or control and communication in the animal and the machine. — M .: “Soviet Radio”, 1958. — 327 p. (in Russian).
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  2. Boris Levin. The theory of stochastic processes and its application in electronics. — M. : “Soviet Radio”, 1957. — 496 p. (in Russian).

Received 06.07.2015

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ISSUE 181, article 2

DOI:https://doi.org/10.15407/kvt181.01.020

Kibern. vyčisl. teh., 2015, Issue 181, pp.

Danik Y.G., Pisarchuk A.A., Tymchyk S.V.

Zhytomyr Military Institute n. a. S.P. Koroljov

SITUATION SYNTHESIS OF AUTOMATED SYSTEM OF COLLECTION AND PROCESSING INFORMATION

Introduction. Modern management processes are characterized by a comprehensive computerization of the process, high dynamic changes in the environment, and the density of the flow of conflict situations (CS) of various kinds. The main factor in achieving the goals of effective management becomes all-encompassing information support provided by the union in a single integrated environment all stakeholders: sensory component; information processing system with the operational staff — forming a system for collecting and processing information (SCPI); consumer information. The results of the application of modern SCPI prove the existence of problems in their design philosophy and mathematical foundations of functioning in solving problems of structural synthesis and processing.
Purpose. Development of the mathematical foundations of situational structure synthesis SCPI and efficient data processing methods in terms of their considerable redundancy, the dynamics of change of the current situation and the flux density of the CS.
Results. Methodology situational structure synthesis automated SCPI should include: formation SCPI information model based on fractal structures; detection and identification of the current CS; situational structural and parametric synthesis SCPI arisen for the CS; estimation of effectiveness of fusion and if necessary correction of previous stages; repeat all stages of the methodology for the next CS.
Formation of SCPI information model based on fractal structures due to their properties: self-similarity, fractional dimension, scale invariance, hierarchy — ensures that the generated model requirements. Implementation of information processing in SCPI based on the method of joint processing of measurement models of self-organization and trees with statistical inference and fuzzy approaches.
Conclusion. Effective implementation of the aims SCPI implemented: introduction ideologies open, distributed, information-management systems that are invariant in structure to the level of control; using technologies protected networks and principles of unification; cyclical processes of collection, storage and processing of information.
Keywords: information system, contingency management, fractal.

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References

  1. Morozov A.O., Kuzmenko G.Ye., Lytvynov V.A. The situational centres. The theory and practice: the collection of articles. Kiev : Vydavnyctvo SP “Intertehnodruk”, 2009, 346 p.
    (in Ukrainian).
  2. Shirman Ya.D. Radio electronic systems. Bases of construction and theory: reference book. Moscov : Radiotehnika, 2007, 512 p. (in Russian).
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  9. Danik Yu.G., Pisarchuk A.A. The technique of structural and parametric synthesis of complex ergatic distributed information management system respondштп to conflict situations. Problems of management and informatics, 2014, no. 2, pp. 80–101. (in Russian).
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    of self-organization of diverse attributes. Cybernetics and Computer Science, 2014, no. 178, pp. 66–81. (in Russian).
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Received 17.05.2015

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ISSUE 181, article 1

DOI:https://doi.org/10.15407/kvt181.01.005

Kibern. vyčisl. teh., 2015, Issue 181, pp.

Fainzilberg L.S., Orikhovska K.B.

International Research and Training Center for Information Technologies and Systems of National Academy of Sciences of Ukraine and Ministry of Education and Science of Ukraine

INFORMATION TECHNOLOGY OF THE ORGANISM ADAPTATION RESERVES ASSESSMENT IN FIELD CONDITIONS

Introduction. Building an effective IT that provides an assessment of the reserve capacity of the organism to physical and emotional overload has both cognitive and practical importance. The relevance of such IT is increasing in our time since it is necessary to provide reliable results in field conditions. This requires prompt, convenient and reliable tools for obtaining test results, which is to be clear not only the decision maker, but also to the examinee that has no medical education.
The purpose of the article is to propose a new information technology for assessing the adequacy the body’s reaction and recovery processes of the cardiovascular system of a human on a set of single-channel ECG parameters.
Methods. The proposed IT includes a set of interacting modules, in particular input module and ECG processing module, which realized on FAZAGRAF® complex. This complex provides recording of the ECG first standard lead and automatic detection of 32 ECG parameters and variability of the cardiac cycle in three states: at rest, immediately after dosage load and after 3 minutes of rest. A distinctive feature of the technology is that decisions on adequate or inadequate response of the organism to physical or emotional overload are realized by two methods — qualitative assessment and quantitative assessment.
Results. It is shown that a qualitative assessment of the reaction to the overload can be carried out on the basis of recognition of patterns’ classes generated by each triplet of measured parameters, and comparing the detected pattern with the dominant classes of each of the parameters. Quantitative assessment can be carried out based on the comparison of the generalized parameter with thresholds.
The algorithm for determining the dominant classes of parameters is proposed. Statistical analysis showed that the probability of appearance of patterns’ classes and generalized parameter values significantly different in the groups of trained and untrained persons. Examples of decision-making of the adequate and inadequate reaction of the organism on the overload are given.
Conclusions. The proposed IT satisfies the formulated requirements to field tools for testing the reserve capacity of the cardiovascular system during physical and emotional overloads.

Keywords: information technology, cardiovascular system, assessment of reserve capacity of the organism.

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Received 28.05.2015