Name
Abstract | ||
|---|---|---|
This paper proposes a new matrix method to solve the inverse problem for the Frobenius-Perron equation. The method can be used to construct a piecewise linear Markov transformation, which approximates the evolution of an unknown dynamical system, based on a sequence of observed probability density functions generated by the system. This particular nonlinear system identification problem is solved using a three-step approach which involves determining the Markov partition, the matrix representation of the Frobenius-Perron operator and finally the corresponding point transformation. A numerical example is used to demonstrate the applicability of the approach. |
Year | Venue | Keywords |
|---|---|---|
2013 | Control Conference | markov processes,matrix algebra,nonlinear control systems,piecewise linear techniques,statistical analysis,markov partition,inverse frobenius-perron problem,matrix method,matrix representation,nonlinear system identification,piecewise linear markov transformation,point transformation,probability density functions,unknown dynamical system,density functional theory,probability density function,inverse problems,sociology,histograms |
Field | DocType | Citations |
Applied mathematics,Mathematical optimization,Markov process,Continuous-time Markov chain,Markov property,Stochastic matrix,Markov chain,Markov partition,Inverse problem,Markov kernel,Mathematics | Conference | 1 |
PageRank | References | Authors |
0.48 | 2 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nie, X. | 1 | 1 | 0.48 |
| Daniel Coca | 2 | 106 | 20.12 |