Multistability in Mittag-Leffler sense of fractional-order neural networks with piecewise constant arguments

Wan, Liguang; Wu, Ailong

doi:10.1016/j.neucom.2018.01.049

Cited by 30 publications

(10 citation statements)

References 52 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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“…From ( [14,34], the system discussed in this paper involves impulses, in which the state functions z i (τ ) are discontinuous in [τ 0 , ∞). Therefore, the method used for the positive invariant sets in [14] cannot be used up directly to this paper.…”

Section: The Invariant Sets and Boundednessmentioning

confidence: 99%

Asymptotic multistability and local S-asymptotic ω-periodicity for the nonautonomous fractional-order neural networks with impulses

Kao

2020

Sci. China Inf. Sci.

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This paper focuses on the investigation of asymptotic multistability and on local S-asymptotic ω-periodicity for nonautonomous fractional-order neural networks (FONNs) with impulses. Several criteria on the existence, uniqueness, and invariant sets of nonautonomous FONNs with impulses are derived by constructing convergent sequences and comparison principles, respectively. In addition, using the Lyapunov direct method, some novel conditions of boundedness and local asymptotic stability of the FONNs discussed are obtained. Also, the sufficient conditions for local S-asymptotic ω-periodicities of the system are presented. Finally, a discussion using two examples verifies the validity of our findings, which imply that global asymptotic stability is a special case of asymptotic multistability.

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“…PCA, as for an upgraded form of timedelay, originates from [31] and [32], and then Akhmet [30] used equivalent integral equations to study the stability of differential equations by constructing the Lyapunov function, which complements the knowledge territory of the solution channels towards the stability issues with PCA. At present, PCA has been gradually applied to various nonlinear dynamical systems [33]- [40], such as Cohen-Grossberg neural networks [33], BAM systems [34], memristor-based dynamic systems [35], cellular neural networks [36], [37], nonlinear differential equations [38], [39], fuzzy neurodynamic systems [40], etc. For the second interference type-neutral term (NT), NT is the interference factor located in the derivative part of a specific system.…”

Section: Introductionmentioning

confidence: 99%

Robustness Analysis of Exponential Stability of Neutral-Type Nonlinear Systems With Multi-Interference

Xie

2021

IEEE Access

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With a view to the unfavorable impact of the inevitable exogenous interferences for the practical engineering and signal transmission, here we focus on the robustness of global exponential stability for nonlinear dynamical system subject to piecewise constant arguments, neutral terms and stochastic disturbances (SNPNDS). A new troublesome problem is that the neutral terms appeared in the derivative part affected on the other two interference factors is not a simple accumulation, so the Lipchitz condition is adopted to establish the ternary transcendental equations. However, different from previous transcendental equations with single or double variables, solving the transcendental equations with three variables becomes the bottleneck again. Hence, the special independent parameters & interdependent variables method targeted for SNPNDS is adopted here: firstly, all relative independent parameters are fixed. Next, the upper bounds of these three interdependent variables are orderly derived by their coupling relationship. Therein, the optimal constraint conditions for piecewise constant arguments and neutral terms are deduced. Through the strategies mentioned above, a class of algebraic problems of estimating three upper bounds by solving transcendental equations with three variables is settled. Besides, the main method ensures the relationship built among the interference factors is mutually restrictive and dynamic. Meanwhile, the optimized constraints make the linkage effect more comprehensive and valid. Furthermore, the established mechanism is practical enough to be generalized to more multivariable systems. Finally, the numerical simulation comparisons are given to illustrate the validity of the derived results.INDEX TERMS Robustness, nonlinear system, neutral term, piecewise constant argument, stochastic disturbance.

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Multistability in Mittag-Leffler sense of fractional-order neural networks with piecewise constant arguments

Cited by 30 publications

References 52 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

Asymptotic multistability and local S-asymptotic ω-periodicity for the nonautonomous fractional-order neural networks with impulses

Robustness Analysis of Exponential Stability of Neutral-Type Nonlinear Systems With Multi-Interference

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