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一种使用主动和参数识别方法对混沌广义Lotka-Volterra生物模型进行稳定性分析的有效同步方法。

An Effective Synchronization Approach to Stability Analysis for Chaotic Generalized Lotka-Volterra Biological Models Using Active and Parameter Identification Methods.

作者信息

Chaudhary Harindri, Khan Ayub, Nigar Uzma, Kaushik Santosh, Sajid Mohammad

机构信息

Department of Mathematics, Jamia Millia Islamia, New Delhi 110025, India.

Department of Mathematics, Deshbandhu College, New Delhi 110019, India.

出版信息

Entropy (Basel). 2022 Apr 9;24(4):529. doi: 10.3390/e24040529.

DOI:10.3390/e24040529
PMID:35455192
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9032272/
Abstract

In this manuscript, we systematically investigate projective difference synchronization between identical generalized Lotka-Volterra biological models of integer order using active control and parameter identification methods. We employ Lyapunov stability theory (LST) to construct the desired controllers, which ensures the global asymptotical convergence of a trajectory following synchronization errors. In addition, simulations were conducted in a MATLAB environment to illustrate the accuracy and efficiency of the proposed techniques. Exceptionally, both experimental and theoretical results are in excellent agreement. Comparative analysis between the considered strategy and previously published research findings is presented. Lastly, we describe an application of our considered combination difference synchronization in secure communication through numerical simulations.

摘要

在本手稿中，我们使用主动控制和参数识别方法，系统地研究了整数阶相同广义洛特卡-沃尔泰拉生物模型之间的投影差分同步。我们采用李雅普诺夫稳定性理论（LST）来构造所需的控制器，这确保了跟踪同步误差的轨迹的全局渐近收敛。此外，在MATLAB环境中进行了仿真，以说明所提技术的准确性和效率。特别地，实验结果和理论结果非常吻合。给出了所考虑策略与先前发表的研究结果之间的对比分析。最后，我们通过数值仿真描述了所考虑的组合差分同步在安全通信中的应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c66/9032272/1a20e6f83c8e/entropy-24-00529-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c66/9032272/7db866cc5f16/entropy-24-00529-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c66/9032272/8a58ec81fe63/entropy-24-00529-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c66/9032272/8b3ee9697540/entropy-24-00529-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c66/9032272/ab109e38c091/entropy-24-00529-g004a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c66/9032272/d8fcdff77ded/entropy-24-00529-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c66/9032272/876078d77748/entropy-24-00529-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c66/9032272/dc4bab95a653/entropy-24-00529-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c66/9032272/451b9648f69f/entropy-24-00529-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c66/9032272/1a20e6f83c8e/entropy-24-00529-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c66/9032272/7db866cc5f16/entropy-24-00529-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c66/9032272/8a58ec81fe63/entropy-24-00529-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c66/9032272/8b3ee9697540/entropy-24-00529-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c66/9032272/ab109e38c091/entropy-24-00529-g004a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c66/9032272/d8fcdff77ded/entropy-24-00529-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c66/9032272/876078d77748/entropy-24-00529-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c66/9032272/dc4bab95a653/entropy-24-00529-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c66/9032272/451b9648f69f/entropy-24-00529-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c66/9032272/1a20e6f83c8e/entropy-24-00529-g009.jpg

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