Formulating Analytical Solution of Network ODE Systems Based on Input Excitations


Susmit Bagchi, Journal of Information Processing Systems Vol. 14, No. 2, pp. 455-468, Apr. 2018  

10.3745/JIPS.03.0092
Keywords: computer networks, Convergent Functions, Dynamic Networks, Ordinary Differential Equations
Fulltext:

Abstract

The concepts of graph theory are applied to model and analyze dynamics of computer networks, biochemical networks and, semantics of social networks. The analysis of dynamics of complex networks is important in order to determine the stability and performance of networked systems. The analysis of non-stationary and nonlinear complex networks requires the applications of ordinary differential equations (ODE). However, the process of resolving input excitation to the dynamic non-stationary networks is difficult without involving external functions. This paper proposes an analytical formulation for generating solutions of nonlinear network ODE systems with functional decomposition. Furthermore, the input excitations are analytically resolved in linearized dynamic networks. The stability condition of dynamic networks is determined. The proposed analytical framework is generalized in nature and does not require any domain or range constraints.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from November 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.




Cite this article
[APA Style]
Susmit Bagchi (2018). Formulating Analytical Solution of Network ODE Systems Based on Input Excitations. Journal of Information Processing Systems, 14(2), 455-468. DOI: 10.3745/JIPS.03.0092.

[IEEE Style]
S. Bagchi, "Formulating Analytical Solution of Network ODE Systems Based on Input Excitations," Journal of Information Processing Systems, vol. 14, no. 2, pp. 455-468, 2018. DOI: 10.3745/JIPS.03.0092.

[ACM Style]
Susmit Bagchi. 2018. Formulating Analytical Solution of Network ODE Systems Based on Input Excitations. Journal of Information Processing Systems, 14, 2, (2018), 455-468. DOI: 10.3745/JIPS.03.0092.