Name
Abstract | ||
|---|---|---|
This article discusses the identification of nonlinear dynamic systems using multi-layer perceptrons (MLPs). It focuses on both structure uncertainty and parameter uncertainty, which have been widely explored in the literature of nonlinear system identification. The main contribution is that an integrated analytic framework is proposed for automated neural network structure selection, parameter identification and hysteresis network switching with guaranteed neural identification performance. First, an automated network structure selection procedure is proposed within a fixed time interval for a given network construction criterion. Then, the network parameter updating algorithm is proposed with guaranteed bounded identification error. To cope with structure uncertainty, a hysteresis strategy is proposed to enable neural identifier switching with guaranteed network performance along the switching process. Both theoretic analysis and a simulation example show the efficacy of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1080/00207721003624550 | Int. J. Systems Science |
Keywords | Field | DocType |
structure uncertainty,automated network structure selection,multiple neural network,network parameter,network construction criterion,automated nonlinear system,guaranteed neural identification performance,guaranteed network performance,automated neural network structure,nonlinear system identification,guaranteed bounded identification error,hysteresis network,neural networks,multi layer perceptron,neural network,nonlinear system,network performance | Mathematical optimization,Nonlinear system,Control theory,Computer science,Nonlinear system identification,Probabilistic neural network,Network switch,Time delay neural network,Artificial neural network,Perceptron,Network performance | Journal |
Volume | Issue | ISSN |
42 | 10 | 0020-7721 |
Citations | PageRank | References |
7 | 0.46 | 14 |
Authors | ||
3 | ||