Exponential stability and extended dissipativity criteria for generalized discrete-time neural networks with additive time-varying delays

Shan, Yaonan; She, Kun; Zhong, Shouming; Zhong, Qishui; Shi, Kaibo; Zhao, Can

doi:10.1016/j.amc.2018.03.101

Cited by 10 publications

(4 citation statements)

References 47 publications

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“…erefore, financial risk exists in every link in the process of enterprise capital flow. As long as there is a risk in one link, it is likely to cause financial crisis [23][24][25][26][27][28][29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Empirical Analysis of Enterprise Financial Management Risk Prediction in View of Associative Memory Neural Network

Cheng

Zhang

2022

Security and Communication Networks

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Enterprise financial analysis has a far-reaching impact on modern enterprise management decision-making and plays a role that cannot be ignored. Financial status is related to the life and death of an enterprise and is the lifeline of an enterprise. Fast and efficient financial analysis can provide reliable and accurate decision-making information support for enterprise investors, operators, creditors, and other organizations and individuals to understand and evaluate the enterprise status and future development potential. With the development of intelligent methods such as associative memory neural network, the research of financial analysis decision support based on artificial intelligence has been paid more and more attention by academia and management, and has made new progress. Efficient and accurate financial risk prediction can help enterprises predict the possible financial risks in the future earlier, facilitate the early detection of problems, and take effective measures to avoid risks or minimize losses. However, most of the existing mature financial risk prediction studies are based on balanced data sets. The research on the classification of unbalanced data sets is not mature and perfect and needs to be further studied. Generally speaking, this paper mainly adopts the research method of cross integration of various disciplines and organically integrates the key theories, methods, and technologies such as default risk management theory, financial index analysis theory, data mining principle, prediction and decision theory, computer technology, multiclassifier integration technology, a variety of enterprise financial risk early warning technology and statistical sampling, carrying out systematic research on enterprise financial risk prediction. This review constructs the enterprise financial risk prediction method system based on heterogeneous data mining technology, mainly including data preprocessing layer, improved nearest neighbor delta increment layer, heterogeneous nearest neighbor extraction layer, and case-based reasoning prediction layer, so as to improve the traditional financial risk prediction method and obtain a new risk classification prediction model. The case-based method has some significant advantages in risk prediction performance, and it also helps to reduce the probability of financial risk.

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Section: Introductionmentioning

confidence: 99%

Empirical Analysis of Enterprise Financial Management Risk Prediction in View of Associative Memory Neural Network

Cheng

Zhang

2022

Security and Communication Networks

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“…Remark 4: It is obvious that the above two theorems are proposed for all α ∈ (0, 1]. The results, of course, are still valid for integer-order chaotic systems with system uncertainties and time-varying delays, which had been discussed in [16], [17], [18], [19], [20].…”

Section: Gmls Of Fnntvd Based On Hybrid Sliding Mode Controllermentioning

confidence: 96%

“…Moreover, considering the limited speed of signal transmission in a network, time-varying delay is unavoidable and should be considered into the model. It is well known that integer order neural networks with time-varying delay have been deeply studied in [16], [17], [18], [19], [20]. However, only few studies explore the fractional-order neural networks with time-varying delay (FNNTVD) [21], [22], n i=1 |x i (t)|, V (t) = x T (t)Qx(t), and so on.)…”

Section: Introductionmentioning

confidence: 99%

Global Mittag-Leffler Synchronization of Fractional-Order Neural Networks With Time-Varying Delay via Hybrid Sliding Mode Control

Yang

Chen

2020

IEEE Access

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In this paper, the global Mittag-Leffler synchronization (GMLS) for fractional-order neural networks with time-varying delay (FNNTVD) is studied based on hybrid sliding mode control (HSMC). First, a class of sliding surface is designed to study the dynamic behavior of FNNTVD by using the integer order Lyapunov direct method, which resolves the problem that the activation function of FNNTVD cannot be applied to the construction of Lyapunov function in the previous literature. Then, a new HSMC with the fixed signal transmission time delay is developed to ensure GMLS of FNNTVD. Next, based on Lyapunov direct method and the designed HSMC, some criteria are put forward to achieve GMLS of FNNTVD. Finally, two examples are given to verify the effectiveness of the proposed synchronization criteria for FNNTVD.

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“…Therefore, we claim that it will be significative to further study almost periodic differential equations. In the field of biological dynamic, several useful researches on APDEs 3428 DEMOU LUO AND QIRU WANG have been published such as hematopoiesis system [9,10,11,19,25,42,43,44], cellular neural networks [14,17,18,20,32,41], systems on time scales [13,38,39,46] and so on.…”

mentioning

confidence: 99%

Dynamic analysis on an almost periodic predator-prey system with impulsive effects and time delays

Luo¹,

Wang²

2021

DCDS-B

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This article is concerned with a generalized almost periodic predatorprey model with impulsive effects and time delays. By utilizing comparison theorem and constructing a feasible Lyapunov functional, we obtain sufficient conditions to guarantee the permanence and global asymptotic stability of the system. By applying Arzelà-Ascoli theorem, we establish the existence and uniqueness of almost-periodic positive solutions. A feasible numerical simulation is provided to explain the suitability of our main criteria.

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Exponential stability and extended dissipativity criteria for generalized discrete-time neural networks with additive time-varying delays

Cited by 10 publications

References 47 publications

Empirical Analysis of Enterprise Financial Management Risk Prediction in View of Associative Memory Neural Network

Empirical Analysis of Enterprise Financial Management Risk Prediction in View of Associative Memory Neural Network

Global Mittag-Leffler Synchronization of Fractional-Order Neural Networks With Time-Varying Delay via Hybrid Sliding Mode Control

Dynamic analysis on an almost periodic predator-prey system with impulsive effects and time delays

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