The general theory of the Quasi-reproducible experiments: How to describe the measured data of complex systems?

Abstract In this paper, we suggest a general theory that enables to describe experiments associated with reproducible or quasi-reproducible data reflecting the dynamical and self-similar properties of a wide class of complex systems. Under complex system we understand a system when the model based on microscopic principles and suppositions about the nature of the matter is absent. This microscopic model is usually determined as “the best fit" model. The behavior of the complex system relatively to a control variable (time, frequency, wavelength, etc.) can be described in terms of the so-called intermediate model (IM). One can prove that the fitting parameters of the IM are associated with the amplitude-frequency response of the segment of the Prony series. The segment of the Prony series including the set of the decomposition coefficients and the set of the exponential functions (with k = 1,2,…,K) is limited by the final mode K. The exponential functions of this decomposition depend on time and are found by the original algorithm described in the paper. This approach serves as a logical continuation of the results obtained earlier in paper [Nigmatullin RR, W. Zhang and Striccoli D. General theory of experiment containing reproducible data: The reduction to an ideal experiment. Commun Nonlinear Sci Numer Simul, 27, (2015), pp 175–192] for reproducible experiments and includes the previous results as a partial case. In this paper, we consider a more complex case when the available data can create short samplings or exhibit some instability during the process of measurements. We give some justified evidences and conditions proving the validity of this theory for the description of a wide class of complex systems in terms of the reduced set of the fitting parameters belonging to the segment of the Prony series. The elimination of uncontrollable factors expressed in the form of the apparatus function is discussed. To illustrate how to apply the theory and take advantage of its benefits, we consider the experimental data associated with typical working conditions of the injection system in a common rail diesel engine. In particular, the flow rate of the injected fuel is considered at different reference rail pressures. The measured data are treated by the proposed algorithm to verify the adherence to the proposed general theory. The obtained results demonstrate the undoubted effectiveness of the proposed theory.

[1]  Milan Marcic,et al.  Sensor for Injection Rate Measurements , 2006, Sensors (Basel, Switzerland).

[2]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[3]  Raoul R. Nigmatullin,et al.  Reduced fractal model for quantitative analysis of averaged micromotions in mesoscale: Characterization of blow-like signals , 2015 .

[4]  Bruce C. Straits,et al.  Approaches to social research , 1993 .

[5]  Dumitru Baleanu,et al.  The First Observation of Memory Effects in the InfraRed (FT-IR) Measurements: Do Successive Measurements Remember Each Other? , 2014, PloS one.

[6]  J. Mendel Lessons in Estimation Theory for Signal Processing, Communications, and Control , 1995 .

[7]  Martin T. Hagan,et al.  Neural network design , 1995 .

[8]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

[9]  J. Kwapień,et al.  Physical approach to complex systems , 2012 .

[10]  J. Bendat,et al.  Random Data: Analysis and Measurement Procedures , 1971 .

[11]  J. A. Tenreiro Machado,et al.  Detection of quasi-periodic processes in complex systems: how do we quantitatively describe their properties? , 2013 .

[12]  Raoul R. Nigmatullin,et al.  Self-similarity principle: the reduced description of randomness , 2013 .

[13]  C. K. Yuen,et al.  Theory and Application of Digital Signal Processing , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[14]  Huba Németh,et al.  Sensitivity analysis of instantaneous fuel injection rate determination for detailed Diesel combustion models , 2013 .

[15]  Raoul R. Nigmatullin,et al.  Detection of quasi-periodic processes in repeated measurements: New approach for the fitting and clusterization of different data , 2014, Commun. Nonlinear Sci. Numer. Simul..

[16]  Domenico Striccoli,et al.  General theory of experiment containing reproducible data: The reduction to an ideal experiment , 2015, Commun. Nonlinear Sci. Numer. Simul..

[17]  Wilhelm Bosch,et al.  The Fuel Rate Indicator: A New Measuring Instrument For Display of the Characteristics of Individual Injection , 1966 .

[18]  Murat Kulahci,et al.  Introduction to Time Series Analysis and Forecasting , 2008 .

[19]  Emmanuel Ifeachor,et al.  Digital Signal Processing: A Practical Approach , 1993 .