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Your search keyword '"SAHOO, JAJATI KESHARI"' showing total 2 results
Start Over Author "SAHOO, JAJATI KESHARI" Topic dynamical systems
2 results on '"SAHOO, JAJATI KESHARI"'

1. Improved finite‐time zeroing neural network for time‐varying division.

Author

Gerontitis, Dimitris, Behera, Ratikanta, Sahoo, Jajati Keshari, and Stanimirović, Predrag S.

Subjects
TIME-varying networks, LINEAR control systems, ERROR functions, DYNAMICAL systems
Abstract

A novel complex varying‐parameter finite‐time zeroing neural network (VPFTZNN) for finding a solution to the time‐dependent division problem is introduced. A comparative study in relation to the zeroing neural network (ZNN) and finite‐time zeroing neural network (FTZNN) is established in terms of the error function and the convergence speed. The error graphs of the VPFTZNN design show promising results and perform better than corresponding ZNN and FTZNN graphs. The proposed dynamical systems are suitable tools for overcoming the division by zero difficulty, which appears in the time‐varying division. An application of the introduced VPFTZNN model in an output tracking control time‐varying linear system is demonstrated. [ABSTRACT FROM AUTHOR]

Published
2021
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2. Simulation of Varying Parameter Recurrent Neural Network with application to matrix inversion.

Author

Stanimirović, Predrag, Gerontitis, Dimitris, Tzekis, Panagiotis, Behera, Ratikanta, and Sahoo, Jajati Keshari

Subjects
*RECURRENT neural networks, *ERROR functions, *TIME-varying systems, *DYNAMICAL systems, *MATRIX inversion, *ERROR rates
Abstract

A class of adaptive recurrent neural networks (RNN) for computing the inverse of a time-varying matrix with accelerated convergence time is defined and considered. The proposed neural dynamic model involves an exponential gain time-varying term in the nonlinear activation of the finite-time Zhang neural network (FTZNN) dynamical equation. Individual models belonging to the proposed class are defined by means of corresponding error functions. It is shown theoretically and experimentally that usage of the exponential nonlinear activation accelerates the convergence rate of the error function compared to previous dynamical systems for solving the time-varying (TV) and time-invariant (TI) matrix inversion. [ABSTRACT FROM AUTHOR]

Published
2021
Full Text
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