We also give some numerical examples to show the applicability and novelty of the results, and compare the results with the corresponding robust stability results derived in the previous literature.
📌 3 Key Takeaways
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Objective & Hypothesis:This paper deals with the problem of robust stability of the class of bidirectional associative memory (BAM) neural networks with multiple time delays.
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Key Findings & Data:Several new sufficient conditions that imply the existence, uniqueness and global robust stability of the equilibrium point for the class of BAM neural networks are obatined by the use of the proper Lyapunov functionals and exploiting the norm properties of the interval matrices.
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Conclusion & Impact:We also give some numerical examples to show the applicability and novelty of the results, and compare the results with the corresponding robust stability results derived in the previous literature.
This paper deals with the problem of robust stability of the class of bidirectional associative memory (BAM) neural networks with multiple time delays. Several new sufficient conditions that imply the existence, uniqueness and global robust stability of the equilibrium point for the class of BAM neural networks are obatined by the use of the proper Lyapunov functionals and exploiting the norm properties of the interval matrices. The derived results basically depend on the system parameters of neural network model and they are independent of the time delays. We also give some numerical examples to show the applicability and novelty of the results, and compare the results with the corresponding robust stability results derived in the previous literature.
Yucel, E. (2017). Novel Conditions for Robust Stability of Bidirectional Associative Memory Neural Networks with Multiple Time Delays. Electrica, Cilt: 17 Sayı: 1, 3227–3238. https://ajindex.com/articles/1017415
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