Synchronization of time invariant uncertain delayed neural networks in finite time via improved sliding mode control
| dc.citation.epage | 240 | |
| dc.citation.issue | 2 | |
| dc.citation.spage | 228 | |
| dc.contributor.affiliation | Урядовий коледж мистецтв | |
| dc.contributor.affiliation | Коледж мистецтв і науки Шрі Рамакрішна | |
| dc.contributor.affiliation | Government Arts College | |
| dc.contributor.affiliation | Sri Ramakrishna College of Arts and Science | |
| dc.contributor.author | Джаянті, Н. | |
| dc.contributor.author | Сантакумарі, Р. | |
| dc.contributor.author | Jayanthi, N. | |
| dc.contributor.author | Santhakumari, R. | |
| dc.coverage.placename | Львів | |
| dc.coverage.placename | Lviv | |
| dc.date.accessioned | 2023-10-24T07:21:55Z | |
| dc.date.available | 2023-10-24T07:21:55Z | |
| dc.date.created | 2021-03-01 | |
| dc.date.issued | 2021-03-01 | |
| dc.description.abstract | У статті досліджується задача часово-скінченної синхронізації складних нейронних мереж із запізнюванням та інваріантною щодо часу невизначеністю шляхом вдосконалення інтегрального керування режимом ковзання. По-перше, комплексні нейронні мережі “ведучий–ведений” перетворюються на дві дійсні нейронні мережі за допомогою методу поділу комплексних нейронних мереж на дійсну та уявну частини. Крім того, члени інтервальної невизначеності комплексних нейронних мереж із запізнюванням перетворюються на дійсні умови невизначеності. По-друге, нова інтегральна поверхня ковзного режиму розроблена з використанням концепції “ведучий–ведений” так, що система помилок може збігатися до нуля за скінченний час вздовж побудованої інтегральної поверхні режиму ковзання. Далі, за допомогою теорії стійкості Ляпунова розроблено відповідне керування режимом ковзання, завдяки якому траєкторії стану системи можуть бути переведені на попередньо задану поверхню режиму ковзання за скінченний час. Нарешті, подано чисельний приклад, який ілюструє ефективність теоретичних результатів. | |
| dc.description.abstract | This paper explores the finite-time synchronization problem of delayed complex valued neural networks with time invariant uncertainty through improved integral sliding mode control. Firstly, the master-slave complex valued neural networks are transformed into two real valued neural networks through the method of separating the complex valued neural networks into real and imaginary parts. Also, the interval uncertainty terms of delayed complex valued neural networks are converted into the real uncertainty terms. Secondly, a new integral sliding mode surface is designed by employing the master-slave concept and the synchronization error of master-slave systems such that the error system can converge to zero in finite-time along the constructed integral sliding mode surface. Next, a suitable sliding mode control is designed by using Lyapunov stability theory such that state trajectories of the system can be driven onto the pre-set sliding mode surface in finite-time. Finally, a numerical example is presented to illustrate the effectiveness of the theoretical results. | |
| dc.format.extent | 228-240 | |
| dc.format.pages | 13 | |
| dc.identifier.citation | Jayanthi N. Synchronization of time invariant uncertain delayed neural networks in finite time via improved sliding mode control / N. Jayanthi, R. Santhakumari // Mathematical Modeling and Computing. — Lviv : Lviv Politechnic Publishing House, 2021. — Vol 8. — No 2. — P. 228–240. | |
| dc.identifier.citationen | Jayanthi N. Synchronization of time invariant uncertain delayed neural networks in finite time via improved sliding mode control / N. Jayanthi, R. Santhakumari // Mathematical Modeling and Computing. — Lviv : Lviv Politechnic Publishing House, 2021. — Vol 8. — No 2. — P. 228–240. | |
| dc.identifier.doi | doi.org/10.23939/mmc2021.02.228 | |
| dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/60396 | |
| dc.language.iso | en | |
| dc.publisher | Видавництво Львівської політехніки | |
| dc.publisher | Lviv Politechnic Publishing House | |
| dc.relation.ispartof | Mathematical Modeling and Computing, 2 (8), 2021 | |
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| dc.relation.references | [24] Zhou J., Lu J. A., Lu J. Pinning adaptive synchronization of a general complex dynamical network. Automatica. 44 (4), 996–1003 (2008). | |
| dc.relation.references | [25] Zhao Y., Li X., Duan P. Observer-based sliding mode control for synchronization of delayed chaotic neural networks with unknown disturbance. Neural Networks. 117, 268–273 (2019). | |
| dc.relation.references | [26] Huang H., Feng G. Synchronization of nonidentical chaotic neural networks with time delays. Neural Networks. 22 (7), 869–874 (2009). | |
| dc.relation.references | [27] Zhang D., Xu J. Projective synchronization of different chaotic time-delayed neural networks based on integral sliding mode controller. Applied Mathematics and Computation. 217 (1), 164–174 (2010). | |
| dc.relation.references | [28] Xiong J. J., Zhang G. B., Wang J. X., Yan T. H. Improved sliding mode control for finite-time synchronization of nonidentical delayed recurrent neural networks. IEEE Transactions on Neural Networks and Learning Systems. 31 (6), 2209–2216 (2019). | |
| dc.relation.referencesen | [1] You X., Song Q., Zhao Z. Existence and finite-time stability of discrete fractional-order complex-valued neural networks with time delays. Neural Networks. 123, 248–260 (2020). | |
| dc.relation.referencesen | [2] Zhang T., Han S., Zhou J. Dynamic behaviours for semi-discrete stochastic Cohen–Grossberg neural networks with time delays. Journal of the Franklin Institute. 357 (17), 13006–13040 (2020). | |
| dc.relation.referencesen | [3] Zhang Z., Guo R., Liu X., Zhong M., Lin C., Chen B. Fixed-time synchronization for complex-valued BAM neural networks with time delays. Asian Journal of Control. 23 (1), 298–314 (2021). | |
| dc.relation.referencesen | [4] Liu P., Li L., Shi K,. Lu J. Pinning stabilization of probabilistic boolean networks with time delays. IEEE Access. 8, 154050–154059 (2020). | |
| dc.relation.referencesen | [5] Wei R., Cao J. Global exponential synchronization of quaternion-valued memristive neural networks with time delays. Nonlinear Analysis: Modelling and Control. 25 (1), 36–56 (2020). | |
| dc.relation.referencesen | [6] Vadivel R., Hammachukiattikul P., Rajchakit G., Ali M. S., Unyong B. Finite-time event-triggered approach for recurrent neural networks with leakage term and its application. Mathematics and Computers in Simulation. 182, 765–790 (2021). | |
| dc.relation.referencesen | [7] Syed Ali M., Hymavathi M. Synchronization of Fractional Order Neutral Type Fuzzy Cellular Neural Networks with Discrete and Distributed Delays via State Feedback Control. Neural Processing Letters. 53, 929–957 (2021). | |
| dc.relation.referencesen | [8] Zhang Z., Guo R., Liu X., Zhong M., Lin C., Chen B. Fixed-time synchronization for complex-valued BAM neural networks with time delays. Asian J. Control. 23, 298–314 (2021). | |
| dc.relation.referencesen | [9] Yang S., Hu C., Yu J., Jiang H. Finite-time cluster synchronization in complex-variable networks with fractional-order and nonlinear coupling. Neural Networks. 135, 212–224 (2021). | |
| dc.relation.referencesen | [10] Duan L., Shi M., Huang C., Fang X. Synchronization in finite-/fixed-time of delayed diffusive complexvalued neural networks with discontinuous activations. Chaos, Solitons & Fractals. 142, 110386 (2021). | |
| dc.relation.referencesen | [11] Hu T., He Z., Zhang X., Zhong S. Finite-time stability for fractional-order complex-valued neural networks with time delay. Applied Mathematics and Computation. 365, 124715 (2020). | |
| dc.relation.referencesen | [12] Wang Z., Liu X. Exponential stability of impulsive complex-valued neural networks with time delay. Mathematics and Computers in Simulation. 156, 143–157 (2019). | |
| dc.relation.referencesen | [13] Chanthorn P., Rajchakit G., Thipcha J., Emharuethai C., Sriraman R., Lim C. P., Ramachandran R. Robust stability of complex-valued stochastic neural networks with time-varying delays and parameter uncertainties. Mathematics. 8 (5), 742 (2020). | |
| dc.relation.referencesen | [14] Syed Ali M., Narayanan G., Shekher V., Alsaedi A., Ahmad B. Global Mittag-Leffler stability analysis of impulsive fractional-order complex-valued BAM neural networks with time varying delays. Communications in Nonlinear Science and Numerical Simulation. 83, 105088 (2020). | |
| dc.relation.referencesen | [15] Li H.-L., Hu C., Cao J., Jiang H., Alsaedi A. Quasi-projective and complete synchronization of fractionalorder complex-valued neural networks with time delays. Neural Networks. 118, 102–109 (2019). | |
| dc.relation.referencesen | [16] Zhang W., Zhang H., Cao J., Alsaadi F. E., Chen D. Synchronization in uncertain fractional-order memristive complex-valued neural networks with multiple time delays. Neural Networks. 110, 186–198 (2019). | |
| dc.relation.referencesen | [17] Panteley E., Lor´ıa A. Synchronization and dynamic consensus of heterogeneous networked systems. IEEE Trans. Automat. Contr. 62 (9), 3758–3773 (2017). | |
| dc.relation.referencesen | [18] Wang X. F., Chen G. R. Synchronization in scale-free dynamical networks: robustness and fragility. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Application. 49 (1), 54–62 (2002). | |
| dc.relation.referencesen | [19] Sun Y. Z., Li W., Ruan J. Generalized outer synchronization between complex dynamical networks with time delay and noise perturbation. Communications in Nonlinear Science and Numerical Simulation. 18 (4), 989–998 (2013). | |
| dc.relation.referencesen | [20] Li C. G., Chen G. R. Phase synchronization in small-world networks of chaotic oscillators. Physica A: Statistical Mechanics and its Applications. 341, 73–79 (2004). | |
| dc.relation.referencesen | [21] Cao J., Ho D. W. C., Yang Y. Q. Projective synchronization of a class of delayed chaotic systems via impulsive control. Physics Letters A. 373, 3128–3133 (2009). | |
| dc.relation.referencesen | [22] Li C. D., Liao X. F., Wong K. W. Chaotic lag synchronization of coupled time-delayed systems and its applications in secure communication. Physica D: Nonlinear Phenomena. 194 (3–4), 187–202 (2004). | |
| dc.relation.referencesen | [23] Zhou J., Lu J. A., L¨u J. Adaptive synchronization of an uncertain complex dynamical network. IEEE Transactions on Automatic Control. 51 (4), 1339–1344 (2006). | |
| dc.relation.referencesen | [24] Zhou J., Lu J. A., Lu J. Pinning adaptive synchronization of a general complex dynamical network. Automatica. 44 (4), 996–1003 (2008). | |
| dc.relation.referencesen | [25] Zhao Y., Li X., Duan P. Observer-based sliding mode control for synchronization of delayed chaotic neural networks with unknown disturbance. Neural Networks. 117, 268–273 (2019). | |
| dc.relation.referencesen | [26] Huang H., Feng G. Synchronization of nonidentical chaotic neural networks with time delays. Neural Networks. 22 (7), 869–874 (2009). | |
| dc.relation.referencesen | [27] Zhang D., Xu J. Projective synchronization of different chaotic time-delayed neural networks based on integral sliding mode controller. Applied Mathematics and Computation. 217 (1), 164–174 (2010). | |
| dc.relation.referencesen | [28] Xiong J. J., Zhang G. B., Wang J. X., Yan T. H. Improved sliding mode control for finite-time synchronization of nonidentical delayed recurrent neural networks. IEEE Transactions on Neural Networks and Learning Systems. 31 (6), 2209–2216 (2019). | |
| dc.rights.holder | © Національний університет “Львівська політехніка”, 2021 | |
| dc.subject | керування режимом ковзання | |
| dc.subject | поверхня режиму ковзання | |
| dc.subject | часова невизначеність | |
| dc.subject | часова затримка | |
| dc.subject | нейронні мережі | |
| dc.subject | sliding mode control | |
| dc.subject | sliding mode surface | |
| dc.subject | time-invariant uncertainty | |
| dc.subject | time delay | |
| dc.subject | neural networks | |
| dc.title | Synchronization of time invariant uncertain delayed neural networks in finite time via improved sliding mode control | |
| dc.title.alternative | Синхронізація інваріантних щодо часу невизначених нейронних мереж із затримкою на скінченний час за рахунок вдосконаленого керування режимом ковзання | |
| dc.type | Article |
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