Stable Synchronization in Fuzzy Recurrent Neural Networks within a Fixed Time Frame | ||
| Journal of AI and Data Mining | ||
| دوره 12، شماره 4، بهمن 2024، صفحه 545-566 اصل مقاله (1.84 M) | ||
| نوع مقاله: Technical Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2024.14138.2647 | ||
| نویسنده | ||
| Farnaz Sabahi* | ||
| Department of Electrical Engineering, Faculty of Electrical and Computer Engineering, Urmia University, Urmia, Iran. | ||
| چکیده | ||
| This paper explores fixed-time synchronization for discontinuous fuzzy delay recurrent neural networks (DFRNNs) with time-varying delays. Based on a generalized variable transformation, the error system has been developed to effectively manage discontinuities in neural systems. This research addresses the fixed-time stability problem using a novel discontinuous state-feedback control input and a simple switching adaptive control scheme. The proposed method ensures robust synchronization of the drive and response neural systems within a fixed time. Practical applications of this work include improvements in protocols for secure communications, robotic control systems, and intelligent control frameworks over dynamic systems. A numerical example substantiates the theoretical claims, demonstrating the strengths of the proposed approach. The results show fixed-time convergence of error margins to zero, ensuring unbiased performance within a predefined timeframe, independent of initial conditions. | ||
| کلیدواژهها | ||
| Discontinuous neural networks؛ Fixed-time synchronization؛ Lyapunov function؛ Time-varying delays | ||
| مراجع | ||
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