具有比例延迟的模糊双向联想记忆神经网络的全局耗散

IF 1.9 4区数学 Q1 MATHEMATICS

Iranian Journal of Fuzzy Systems Pub Date : 2021-04-01 DOI:10.22111/IJFS.2021.5914

C. Aouiti, R. Sakthivel, F. Touati

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引用次数: 9

摘要

本文旨在研究具有比例延迟的模糊双向联想记忆神经网络(FBAMNNs-简称FBAMNNs)的全局耗散问题。通过Lyapunov泛函(LFs-简称)和线性矩阵不等式(LMI-简称)方法，我们得到了保证模型全局耗散率和全局指数耗散率的新的充分条件。此外，还考虑了两种不同类型的激活函数，包括一般有界激活函数和lipschitz型激活函数。此外，还给出了全局吸引集和全局指数吸引集。最后，给出了两个数值算例，说明了所得结果的有效性。

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Global dissipativity of fuzzy bidirectional associative memory neural networks with proportional delays

This article aimed to investigate the problem of global dissipativity of Fuzzy Bidirectional Associative Memory Neural Networks (FBAMNNs- for short) with proportional delays. Via Lyapunov Functionals (LFs- for short) and Linear Matrix Inequality (LMI- for short) approach, we obtained new sufficient conditions to guarantee the global dissipativity and global exponential dissipativity of the proposed model. In addition, two different types of activation functions are considered, including general bounded and Lipschitz-type activation functions. Moreover, the globally attractive and globally exponentially attractive sets are presented. Lastly, two numerical examples are given to illustrate the effectiveness of the developed results.

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来源期刊

Iranian Journal of Fuzzy Systems 数学-数学

CiteScore

3.50

自引率

16.70%

发文量

期刊介绍： The two-monthly Iranian Journal of Fuzzy Systems (IJFS) aims to provide an international forum for refereed original research works in the theory and applications of fuzzy sets and systems in the areas of foundations, pure mathematics, artificial intelligence, control, robotics, data analysis, data mining, decision making, finance and management, information systems, operations research, pattern recognition and image processing, soft computing and uncertainty modeling. Manuscripts submitted to the IJFS must be original unpublished work and should not be in consideration for publication elsewhere.