چكيده لاتين :
This paper concerns the design of a neu ral state observer for nonli near dyna mic systems with
noisy measurement channels and in the presence of small model errors. The proposed observer
consists of three feedforward neural parts, two of which are MLP universal approximators, which
are being trained off-line and the last one being a Linearly Parameterized Neural Network (LPNN),
which is being updated on-line. The off-line trained parts are able to generate state estimations
instantly and almost accurately, if there are not catastrophic errors in the mathematical model
used. The contribution of the on-line adapting part is to compensate the remainder estimation
error due to uncertain parameters and/or unmodeled dynamics. A time delay term is also added
to compensate the arising differential effects in the observer. The proposed observer can learn
the noise cancellation property by using noise corrupted data sets in the MLPיs off-line training.
Simulation results in two case studies show the high effectiveness of the proposed state observing
method.
