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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
We provide conditions based on the parameters of the model.
📌 3 Key Takeaways
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Objective & Hypothesis:In this study, we develop a model of recurrent neural networks with functional dependence on piecewise constant argument of generalized type.
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Key Findings & Data:Using the theoretical results obtained for functional differential equations with piecewise constant argument, we investigate conditions for existence and uniqueness of solutions, bounded solutions, and exponential stability of periodic solutions.
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Conclusion & Impact:We provide conditions based on the parameters of the model.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Recurrent neural networks · functional differential equations · piecewise constant argument
Abstract
In this study, we develop a model of recurrent neural networks with functional dependence on piecewise constant argument of generalized type. Using the theoretical results obtained for functional differential equations with piecewise constant argument, we investigate conditions for existence and uniqueness of solutions, bounded solutions, and exponential stability of periodic solutions. We provide conditions based on the parameters of the model.
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Cite This Article
AKHMET, M., ÇİNÇİN, D. A., CENGİZ, N. (2018). Exponential stability of periodic solutions of recurrent neural networks with functional dependence on piecewise constant argument. Turkish Journal of Mathematics, Cilt: 42 - Sayı: 1, 272–292. https://ajindex.com/articles/723144