Biblio

A comparative analysis of the classical and approximation methods of spectral analysis of fast-variable processes in technical systems is carried out. It is shown that the approximation methods make it possible to substantially remove the contradiction between the requirements for spectrum smoothing and its frequency resolution. On practical examples of vibroacoustic signals, the effectiveness of approximation methods is shown. The Prony method was used to process the time series. The interactive frequency segmentation method and the direct identification method were used for approximation and frequency characteristics.

The new switching architecture of the neural networks was proposed. The switching neural networks consist of the neurons and the switchers. The goal is to reduce expenses on the artificial neural network design and training. For realization of complex models, algorithms and methods of management the neural networks of the big size are required. The number of the interconnection links “everyone with everyone” grows with the number of neurons. The training of big neural networks requires the resources of supercomputers. Time of training of neural networks also depends on the number of neurons in the network. Switching neural networks are divided into fragments connected by the switchers. Training of switcher neuron network is provided by fragments. On the basis of switching neural networks the devices of associative memory were designed with the number of neurons comparable to the human brain.