Spectral Signal Simulation as a Scientific and Practical Task in the Training of Engineers

Смирнова, Елена Александровна; Syuzev, Vladimir; Gurenko, Vladimir; Bychkov, Boris

doi:10.21125/inted.2019.1120

Cited by 1 publication

(1 citation statement)

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“…An effective method of solving the limitations of experiment parameters control and essential acceleration of the obtaining the necessary amount of input data process is the simulation of signals [3]. In this case, a necessary requirement for the signal synthesis system along with the adequacy of the model is the performance of data generation, since a critical requirement in the validation of digital signal processing system is its operation in real time.…”

Section: Introductionmentioning

confidence: 99%

Simulation model parameter optimization method for multidimensional signals

Rozanov,

Sotnikov,

Kim

2024

J. Phys.: Conf. Ser.

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The paper describes an approach to minimize the number of simulation experiments of multidimensional signals by means of a regression neural network. Multivariate signal simulation systems are in demand for testing real-time computing systems, but they mostly have a wide vector of model parameters. The formation of parameter vector for the simulation model of multidimensional signals, which ensures an adequate solution to the problem of signal processing obtained as a result of the model operation, is an urgent task. The authors propose a method of heuristic optimization of input parameters, implemented by means of machine learning, which reduces the searching of values for the given optimization function. In this study the ability to perform real-time signal simulations is the optimization feature. The research outcomes are the description of the neural network, the rationale for its configuration, and the training and validation results.

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Section: Introductionmentioning

confidence: 99%