9 research outputs found
Complex-valued Adaptive System Identification via Low-Rank Tensor Decomposition
Machine learning (ML) and tensor-based methods have been of significant
interest for the scientific community for the last few decades. In a previous
work we presented a novel tensor-based system identification framework to ease
the computational burden of tensor-only architectures while still being able to
achieve exceptionally good performance. However, the derived approach only
allows to process real-valued problems and is therefore not directly applicable
on a wide range of signal processing and communications problems, which often
deal with complex-valued systems. In this work we therefore derive two new
architectures to allow the processing of complex-valued signals, and show that
these extensions are able to surpass the trivial, complex-valued extension of
the original architecture in terms of performance, while only requiring a
slight overhead in computational resources to allow for complex-valued
operations
Enhanced Nonlinear System Identification by Interpolating Low-Rank Tensors
Function approximation from input and output data is one of the most
investigated problems in signal processing. This problem has been tackled with
various signal processing and machine learning methods. Although tensors have a
rich history upon numerous disciplines, tensor-based estimation has recently
become of particular interest in system identification. In this paper we focus
on the problem of adaptive nonlinear system identification solved with
interpolated tensor methods. We introduce three novel approaches where we
combine the existing tensor-based estimation techniques with multidimensional
linear interpolation. To keep the reduced complexity, we stick to the concept
where the algorithms employ a Wiener or Hammerstein structure and the tensors
are combined with the well-known LMS algorithm. The update of the tensor is
based on a stochastic gradient decent concept. Moreover, an appropriate step
size normalization for the update of the tensors and the LMS supports the
convergence. Finally, in several experiments we show that the proposed
algorithms almost always clearly outperform the state-of-the-art methods with
lower or comparable complexity.Comment: 12 pages, 4 figures, 3 table
Adaptive System Identification via Low-Rank Tensor Decompositi
Tensor-based estimation has been of particular interest of the scientific community for several years now. While showing promising results on system estimation and other tasks, one big downside is the tremendous amount of computational power and memory required – especially during training – to achieve satisfactory performance. We present a novel framework for different classes of nonlinear systems, that allows to significantly reduce the complexity by introducing a least-mean-squares block before, after, or between tensors to reduce the necessary dimensions and rank required to model a given system. Our simulations show promising results that outperform traditional tensor models, and achieve equal performance to comparable algorithms for all problems considered while requiring significantly less operations per time step than either of the state-of-the-art architectures
Real-Time Processing of Laser-Ultrasonic Signals
Laser-ultrasonic testing is an advanced measurement technique in material characterization. One specific application of this technique is the detection of temperature-dependent changes in the microstructure of metallic samples. During such a measurement series, usually the detected ultrasound signals are just captured, but not analyzed. As it is beneficial if the analysis results are already available during the measurements, in this thesis a real-time processing system for the ultrasound signals was developed. The development board Red Pitaya was used for the signal capturing and most parts of the data processing. Additionally, a PC application with a graphical user interface was implemented for remote-controlling the board and visualizing the results in real-time. The proper functioning of the system was ensured during the implementation by accompanying simulations and tests in the real measurement environment. These experiments showed that the performance requirements for real-time processing were fulfilled.Author Thomas PairederUniversität Linz, Masterarbeit, 2017(VLID)240639