USE OF NEURAL NETWORKS FOR SOLVING PROBLEMS OF NON-CONNECTING PROBLEMS AND SOLUTION OF COMPOSITE COMPARTMENT EQUATIONS OF ELECTRIC TRANSPORT OPERATION

Keywords: Zero Neural Network, Electric Transport, Numerical Algorithms, Reliable Stability

Abstract

The use of neural networks to solve the problems of insolubility and the solution of complex computational equations becomes a common practice in academic circles and industry. It has been shown that, despite the complexity, these problems can be formulated as a set of equations, and the key is to find zeros from them. Zero Neural Networks (ZNNs), as a class of neural networks specially designed to find zeros of equations, have played an indispensable role in online decision-changing problems over time in recent years, and many fruitful research results have been documented in literature. The purpose of this article is to provide a comprehensive overview of ZNN studies, including ZNN continuous time and discrete time models for solving various problems, and their application in motion planning and superfluous manipulator management, chaotic system tracking, or even population control in mathematical biological sciences. Considering the fact that real-time performance is in demand for time-varying problems in practice, analysis of the stability and convergence of various ZNN models with continuous time is considered in a unified form in detail. In the case of solving the problems of discrete time, procedures are summarized for how to discriminate a continuous ZNN model and methods for obtaining an accuracy decision.

Approaches based on the neural network to address various nodal tasks have attracted considerable attention in many areas. For example, an adaptive fuzzy controller based on a neural network is constructed for a class of nonlinear systems with discrete time with a dead zone with discrete time in. An applied decentralized circuit, based on a neural network, is presented for multiple nonlinear input and multiple output systems (MIMO) using the methods of the reverse step in. Such a scheme guarantees a uniform limiting limit of all signals in a closed system relative to the average square. In order to overcome the structural complexity of the nonlinear feedback structure, uses the method of dividing variables for the decomposition of unknown functions of all state variables into the sum of smooth functions of each dynamic error.

Author Biographies

D. Zubenko, O.M. Beketov National University of Urban Economy in Kharkiv

Ph.D., Associate Professor

V. Linkov, O.M. Beketov National University of Urban Economy in Kharkiv

Ph.D., Associate Professor

References

Liu , Y.-J., Tong , S., Li , D.-J., Gao , Y. (2016) Fuzzy adaptive control with state observer for a class of nonlinear discrete-time systems with input constraint, IEEE Trans. Fuzzy Syst. 24 (5), 1147–1158 .

Liu , Y.-J., Tong , S. (2015) Adaptive fuzzy identification and control for a class of non- linear pure-feedback MIMO systems with unknown dead zones, IEEE Trans. Fuzzy Syst. 23 (5), 1387–1398.

Wang , H., Chen , B., Liu , K., Liu , X., Lin , C. (2014) Adaptive neural tracking control for a class of nonstrict-feedback stochastic nonlinear systems with unknown backlash-like hysteresis, IEEE Trans. Neural Netw. Learn. Syst. 25(5), 947–958.

Liu , Y.-J., Tong , S. (2016) Optimal control-based adap-tive NN design for a class of nonlinear discrete-time block-triangular systems, IEEE Trans. Cybern. 46 (11), 2670–2680 .

Liu , Y.-J., Li , J. , Tong , S., Chen , C.P. (2016) Neural network control-based adaptive learn- ing design for nonlinear systems with full-state constraints, IEEE Trans. Neu- ral Netw. Learn. Syst. 27 (7), 1562–1571 .

Li , S. , He , J. , Li , Y. , Rafique , M.U. (2017) Distributed recurrent neural networks for co- operative control of manipula-tors: a game-theoretic perspective, IEEE Trans. Neural Netw. Learn. Syst., 28 (2), 415–426.

Li , S. , Chen , S. , Liu , B. , Li , Y. , Liang , Y. (2012) Decentralized kinematic control of a class of collaborative re-dundant manipulators via recurrent neural networks, Neuro-computing, 91, 1–10.

Li , S. , Liu , B. , Li , Y. (2013) Selective positive–negative feedback produces the winner–take-all competition in recurrent neural networks, IEEE Trans. Neural Netw. Learn. Syst., 24 (2), 301–309.

Zhang , Y. , Guo , D. , Luo , Z. , Zhai , K. , Tan , H. (2016) CP-activated WASD neuronet ap- proach to asian popu-lation prediction with abundant experimental verifica- tion, Neu-rocomputing, 198, 48–57 .

Luo , X. , Shang , M. (2016) Efficient extraction of non-negative latent factors from high-dimensional and sparse matri-ces in industrial applications, in: Proceed- ings of the IEEE 16th International Conference on Data Mining, IEEE, 311–319 .

Huang , Y.-A., You , Z.-H. , Li , X., Chen , X., Hu , P., Luo , X. (2016) Construction of reliable protein–protein interac-tion networks using weighted sparse representation based clas-sifier with pseudo substitution matrix representation features, Neu- rocomputing, 218, 131–138 .

Luo , X. , Zhou , M. (2016) Regularizaed extraction of non-negative latent factors from high-dimensional sparse matrices, in: Proceedings of the IEEE In- ternational Conference on Sys-tems, Man, and Cybernetics, IEEE, 0 01221–0 01226 .

Wang , H. , Liu , P.X. , Liu , S. (2017) Adaptive neural synchronization control for bilat- eral teleoperation systems with time delay and backlash-like hysteresis, IEEE Trans. Cy-bern.

Wang , H. , Liu , W., Liu , P.X. , Lam , H. (2016) Adaptive fuzzy decentralized control for a class of interconnected nonlin-ear system with unmodeled dynamics and dead zones, Neuro-computing, 214, 972–980 .

Stanimirovi ´c , P.S. , Živkovi ´c , I.S. , Wei , Y. (2015) Recurrent neural network approach based on the integral repre-sentation of the Drazin inverse, Neural Comput., 27 (10), 2107–2131.

Published
2019-01-25
How to Cite
ZubenkoD., & LinkovV. (2019). USE OF NEURAL NETWORKS FOR SOLVING PROBLEMS OF NON-CONNECTING PROBLEMS AND SOLUTION OF COMPOSITE COMPARTMENT EQUATIONS OF ELECTRIC TRANSPORT OPERATION. Municipal Economy of Cities, 1(147), 128-130. Retrieved from https://khg.kname.edu.ua/index.php/khg/article/view/5363