Non-parametric Identification of Homogeneous Dynamical Systems

International audienceThe aim of this study is to design a non-parametric identifier for homogeneous systems based on a class of artificial neural networks with continuous dynamics. The identification algorithm is developed for input-affine systems with uncertain gains and diverse degrees of homogeneity. One of the main contributions of this study is the extension of the universal approximation property of neural networks for continuous homogeneous systems. Another contribution is the development of a differential non-parametric identifier based on the novel concept of homogeneous neural networks. The adjustment laws for the weights are obtained from a Lyapunov stability analysis taking homogeneity properties of the system into account. The ultimate boundedness of the origin for the identification error is demonstrated using the persistent excitation condition. The effectiveness of the proposed identifier is verified by the simulation of the three-tank homogeneous model. In this example, the proposed identification scheme is compared with a classical ANN identifier, and we present a statistical analysis of such comparison. It is shown in simulations that the identification error of the proposed homogeneous algorithm has faster convergence and less oscillations

Структурно-параметричний синтез згорткових нейронних мереж при наявності завад у вхідних даних

The purpose of this work is to create and develop the approach on structural parametric synthesis of convolutional neural network to receive the unique CNN architecture with the good image recognition accuracy. The paper deals with the methods of processing and classification of graphic images using convolutional neural networks and mathematical algorithms for their support. Using researches, there shown that for the proper use of such system it’s requires compliance with special technical conditions. Today, in modern convolutional neural networks for the independent processing of graphic data there is a problem of lack of accuracy in the selection of special criteria. The urgency of this problem over time is only increasing due to the proliferation of the problem of digital identification. In order to increase the accuracy of the results of the work, there designed system includes the algorithm of input data preparation, generating the neural network architecture and configuration its global and local parameters with means of structural parametric synthesis algorithms. Also, there were done relative surveys and tests as well as implemented all the algorithms by means of programming

Damage classification and estimation in experimental structures using time series analysis and pattern recognition

Peer reviewedPreprin

Time-varying parametric modelling and time-dependent spectral characterisation with applications to EEG signals using multi-wavelets

A new time-varying autoregressive (TVAR) modelling approach is proposed for nonstationary signal processing and analysis, with application to EEG data modelling and power spectral estimation. In the new parametric modelling framework, the time-dependent coefficients of the TVAR model are represented using a novel multi-wavelet decomposition scheme. The time-varying modelling problem is then reduced to regression selection and parameter estimation, which can be effectively resolved by using a forward orthogonal regression algorithm. Two examples, one for an artificial signal and another for an EEG signal, are given to show the effectiveness and applicability of the new TVAR modelling method