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Improved finite‐time zeroing neural network for time‐varying division
Author(s) -
Gerontitis Dimitris,
Behera Ratikanta,
Sahoo Jajati Keshari,
Stanimirović Predrag S.
Publication year - 2021
Publication title -
studies in applied mathematics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.164
H-Index - 46
eISSN - 1467-9590
pISSN - 0022-2526
DOI - 10.1111/sapm.12354
Subject(s) - division (mathematics) , artificial neural network , convergence (economics) , control theory (sociology) , computer science , tracking (education) , mathematics , control (management) , artificial intelligence , arithmetic , economics , economic growth , psychology , pedagogy
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.