1
|
Wang J, Zhu S, Mu C, Liu X, Wen S. Unified analysis on multistablity of fraction-order multidimensional-valued memristive neural networks. Neural Netw 2024; 179:106498. [PMID: 38986183 DOI: 10.1016/j.neunet.2024.106498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 04/29/2024] [Accepted: 06/26/2024] [Indexed: 07/12/2024]
Abstract
This article provides a unified analysis of the multistability of fraction-order multidimensional-valued memristive neural networks (FOMVMNNs) with unbounded time-varying delays. Firstly, based on the knowledge of fractional differentiation and memristors, a unified model is established. This model is a unified form of real-valued, complex-valued, and quaternion-valued systems. Then, based on a unified method, the number of equilibrium points for FOMVMNNs is discussed. The sufficient conditions for determining the number of equilibrium points have been obtained. By using 1-norm to construct Lyapunov functions, the unified criteria for multistability of FOMVMNNs are obtained, these criteria are less conservative and easier to verify. Moreover, the attraction basins of the stable equilibrium points are estimated. Finally, two numerical simulation examples are provided to verify the correctness of the results.
Collapse
Affiliation(s)
- Jiarui Wang
- School of Mathematics, China University of Mining and Technology, Xuzhou, 221116, China.
| | - Song Zhu
- School of Mathematics, China University of Mining and Technology, Xuzhou, 221116, China.
| | - Chaoxu Mu
- School of Electrical and Automation Engineering, Tianjin University, Tianjin, 300072, China.
| | - Xiaoyang Liu
- School of Computer Science and Technology, Jiangsu Normal University, Xuzhou, 221116, China.
| | - Shiping Wen
- Centre for Artificial Intelligence, University of Technology Sydney, Ultimo, NSW 2007, Australia.
| |
Collapse
|
2
|
Wang J, Zhu S, Liu X, Wen S. Mittag-Leffler stability of fractional-order quaternion-valued memristive neural networks with generalized piecewise constant argument. Neural Netw 2023; 162:175-185. [PMID: 36907007 DOI: 10.1016/j.neunet.2023.02.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/28/2023] [Accepted: 02/21/2023] [Indexed: 03/06/2023]
Abstract
This paper studies the global Mittag-Leffler (M-L) stability problem for fractional-order quaternion-valued memristive neural networks (FQVMNNs) with generalized piecewise constant argument (GPCA). First, a novel lemma is established, which is used to investigate the dynamic behaviors of quaternion-valued memristive neural networks (QVMNNs). Second, by using the theories of differential inclusion, set-valued mapping, and Banach fixed point, several sufficient criteria are derived to ensure the existence and uniqueness (EU) of the solution and equilibrium point for the associated systems. Then, by constructing Lyapunov functions and employing some inequality techniques, a set of criteria are proposed to ensure the global M-L stability of the considered systems. The obtained results in this paper not only extends previous works, but also provides new algebraic criteria with a larger feasible range. Finally, two numerical examples are introduced to illustrate the effectiveness of the obtained results.
Collapse
Affiliation(s)
- Jingjing Wang
- School of Mathematics, China University of Mining and Technology, Xuzhou, 221116, China.
| | - Song Zhu
- School of Mathematics, China University of Mining and Technology, Xuzhou, 221116, China.
| | - Xiaoyang Liu
- School of Computer Science and Technology, Jiangsu Normal University, Xuzhou, 221116, China.
| | - Shiping Wen
- Centre for Artificial Intelligence, University of Technology Sydney, Ultimo, NSW 2007, Australia.
| |
Collapse
|
3
|
Multiple asymptotical ω-periodicity of fractional-order delayed neural networks under state-dependent switching. Neural Netw 2023; 157:11-25. [DOI: 10.1016/j.neunet.2022.09.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Revised: 09/28/2022] [Accepted: 09/29/2022] [Indexed: 11/06/2022]
|
4
|
Mittag-Leffler Type Stability of Delay Generalized Proportional Caputo Fractional Differential Equations: Cases of Non-Instantaneous Impulses, Instantaneous Impulses and without Impulses. Symmetry (Basel) 2022. [DOI: 10.3390/sym14112290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
In this paper, nonlinear differential equations with a generalized proportional Caputo fractional derivative and finite delay are studied in this paper. The eventual presence of impulses in the equations is considered, and the statement of initial value problems in three cases is defined: namely non-instantaneous impulses, instantaneous impulses and no impulses. The relations between these three cases are discussed. Additionally, some stability properties are investigated. We apply the Mittag–Leffler function which plays a vital role and which gives well-known bounds on the norm of the solutions. The symmetry of this function about a line and the bounds is a property that plays an important role in stability. Several sufficient conditions are presented via appropriate new comparison results and the modified Razumikhin method. The results generalize several known results in the literature.
Collapse
|
5
|
Novel controller design for finite-time synchronization of fractional-order memristive neural networks. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.09.118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
6
|
Stability of Memristor-based Fractional-order Neural Networks with Mixed Time-delay and Impulsive. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-11061-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
|
7
|
Xiao J, Zhong S, Wen S. Unified Analysis on the Global Dissipativity and Stability of Fractional-Order Multidimension-Valued Memristive Neural Networks With Time Delay. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:5656-5665. [PMID: 33950847 DOI: 10.1109/tnnls.2021.3071183] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The unified criteria are analyzed on the global dissipativity and stability for the delayed fractional-order systems of multidimension-valued memristive neural networks (FSMVMNNs) in this article. First, based on the comprehensive knowledge about multidimensional algebra, fractional derivatives, and nonsmooth analysis, we establish the unified model for the studied FSMVMNNs in order to propose a more uniform method to analyze the dynamic behaviors of multidimensional neural networks. Then, by mainly applying the Lyapunov method, employing several new lemmas, and solving some mathematical difficulties, without any separation, we acquire the unified and concise criteria. The derived criteria have many advantages in a smaller calculation, lower conservatism, more diversity, and higher flexibility. Finally, we provide two numerical examples to express the availability and improvements of the theoretical results.
Collapse
|
8
|
Linkage-constraint Criteria for Robust Exponential Stability of Nonlinear BAM System with Derivative Contraction Coefficients and Piecewise Constant Arguments. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.08.078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
|
9
|
Luo D, Zhang Y, Li J. Research on Several Key Problems of Medical Image Segmentation and Virtual Surgery. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:3463358. [PMID: 35494211 PMCID: PMC9017556 DOI: 10.1155/2022/3463358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 03/02/2022] [Accepted: 03/04/2022] [Indexed: 11/29/2022]
Abstract
Medical images play an important role in modern medical diagnosis. Many clinicians make correct and appropriate diagnosis and treatment plans by means of medical images. With the development of science and technology, the application of medical image needs not only to simply read the image, but also to fuse advanced technology to analyze and process the image from a deeper level, such as the proposal of virtual surgery. Therefore, this article focuses on several key issues of medical image segmentation and virtual surgery. First, medical images are preprocessed by gray level transformation, interpolation, and noise elimination techniques. Second, level set model-based segmentation algorithm is adopted and improved. Finally, a constrained Delaunay tetrahedron method based on a point-by-point insertion method is proposed to reconstruct the tetrahedron mesh model. In order to eliminate the thin element, the tetrahedron mesh model is optimized. The simulation results show that this article improves the segmentation algorithm based on the level set model, which effectively improves the contradiction between the convergence accuracy and the convergence speed of the algorithm. The proposed tetrahedral mesh reconstruction algorithm realizes the generation of tetrahedral finite element meshes with complex boundaries and improves the quality of the volume model by optimizing the model.
Collapse
Affiliation(s)
- Dan Luo
- School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, Liaoning, China
| | - Yu Zhang
- School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, Liaoning, China
| | - Jia Li
- School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, Liaoning, China
| |
Collapse
|
10
|
Viera-Martin E, Gómez-Aguilar JF, Solís-Pérez JE, Hernández-Pérez JA, Escobar-Jiménez RF. Artificial neural networks: a practical review of applications involving fractional calculus. THE EUROPEAN PHYSICAL JOURNAL. SPECIAL TOPICS 2022; 231:2059-2095. [PMID: 35194484 PMCID: PMC8853315 DOI: 10.1140/epjs/s11734-022-00455-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Accepted: 01/13/2022] [Indexed: 05/13/2023]
Abstract
In this work, a bibliographic analysis on artificial neural networks (ANNs) using fractional calculus (FC) theory has been developed to summarize the main features and applications of the ANNs. ANN is a mathematical modeling tool used in several sciences and engineering fields. FC has been mainly applied on ANNs with three different objectives, such as systems stabilization, systems synchronization, and parameters training, using optimization algorithms. FC and some control strategies have been satisfactorily employed to attain the synchronization and stabilization of ANNs. To show this fact, in this manuscript are summarized, the architecture of the systems, the control strategies, and the fractional derivatives used in each research work, also, the achieved goals are presented. Regarding the parameters training using optimization algorithms issue, in this manuscript, the systems types, the fractional derivatives involved, and the optimization algorithm employed to train the ANN parameters are also presented. In most of the works found in the literature where ANNs and FC are involved, the authors focused on controlling the systems using synchronization and stabilization. Furthermore, recent applications of ANNs with FC in several fields such as medicine, cryptographic, image processing, robotic are reviewed in detail in this manuscript. Works with applications, such as chaos analysis, functions approximation, heat transfer process, periodicity, and dissipativity, also were included. Almost to the end of the paper, several future research topics arising on ANNs involved with FC are recommended to the researchers community. From the bibliographic review, we concluded that the Caputo derivative is the most utilized derivative for solving problems with ANNs because its initial values take the same form as the differential equations of integer-order.
Collapse
Affiliation(s)
- E. Viera-Martin
- Tecnológico Nacional de México/CENIDET, Interior Internado Palmira S/N, Col. Palmira, C.P. 62490 Cuernavaca, Morelos Mexico
| | - J. F. Gómez-Aguilar
- CONACyT-Tecnológico Nacional de México/CENIDET, Interior Internado Palmira S/N, Col. Palmira, C.P. 62490 Cuernavaca, Morelos Mexico
| | - J. E. Solís-Pérez
- Escuela Nacional de Estudios Superiores Unidad Juriquilla, Universidad Nacional Autónoma de México, Boulevard Juriquilla 3001, Juriquilla La Mesa, C.P. 76230 Juriquilla, Querétaro Mexico
| | - J. A. Hernández-Pérez
- Universidad Autónoma del Estado de Morelos/Centro de Investigación en Ingeniería y Ciencias Aplicadas, Av. Universidad No. 1001, Col Chamilpa, C.P. 62209 Cuernavaca, Morelos Mexico
| | - R. F. Escobar-Jiménez
- Tecnológico Nacional de México/CENIDET, Interior Internado Palmira S/N, Col. Palmira, C.P. 62490 Cuernavaca, Morelos Mexico
| |
Collapse
|
11
|
Xiao J, Cao J, Cheng J, Wen S, Zhang R, Zhong S. Novel Inequalities to Global Mittag-Leffler Synchronization and Stability Analysis of Fractional-Order Quaternion-Valued Neural Networks. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021; 32:3700-3709. [PMID: 32997634 DOI: 10.1109/tnnls.2020.3015952] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
This article is concerned with the problem of the global Mittag-Leffler synchronization and stability for fractional-order quaternion-valued neural networks (FOQVNNs). The systems of FOQVNNs, which contain either general activation functions or linear threshold ones, are successfully established. Meanwhile, two distinct methods, such as separation and nonseparation, have been employed to solve the transformation of the studied systems of FOQVNNs, which dissatisfy the commutativity of quaternion multiplication. Moreover, two novel inequalities are deduced based on the general parameters. Compared with the existing inequalities, the new inequalities have their unique superiorities because they can make full use of the additional parameters. Due to the Lyapunov theory, two novel Lyapunov-Krasovskii functionals (LKFs) can be easily constructed. The novelty of LKFs comes from a wider range of parameters, which can be involved in the construction of LKFs. Furthermore, mainly based on the new inequalities and LKFs, more multiple and more flexible criteria are efficiently obtained for the discussed problem. Finally, four numerical examples are given to demonstrate the related effectiveness and availability of the derived criteria.
Collapse
|
12
|
Xu H. Intelligent system for university legal education based on machine learning. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-189311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
University legal education is of great significance to the personal development and social stability of college students. At present, there are certain problems in the traditional teaching system, which has led to inefficient university legal education. In order to improve the legal teaching effect of the university, based on machine learning and neural networks, this paper integrates and optimizes the original hardware and software and operation process, and further highlights the functions of interconnection and sharing, automatic sensing, real-time recording, interactive feedback, dynamic supervision, and intelligent analysis, which greatly facilitates the evaluation of teaching at all levels. In particular, this study uses big data technology to conduct an intelligent analysis of data completeness, multimedia application rate, system execution, and average test scores, and scientifically evaluates the implementation of basic-level education systems and the effectiveness of education, which can effectively solve the problems of quantitative formalization and qualitative subjectivity of current education evaluation from a technical level. In addition, this study designs a control experiment to analyze the system performance. The research results show that the model proposed in this paper has a certain effect.
Collapse
Affiliation(s)
- Hesheng Xu
- Department of Law, Zhejiang University City College, Hangzhou, China
| |
Collapse
|
13
|
Chen MR, Chen BP, Zeng GQ, Lu KD, Chu P. An adaptive fractional-order BP neural network based on extremal optimization for handwritten digits recognition. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2018.10.090] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
|
14
|
Chen L, Yin H, Huang T, Yuan L, Zheng S, Yin L. Chaos in fractional-order discrete neural networks with application to image encryption. Neural Netw 2020; 125:174-184. [DOI: 10.1016/j.neunet.2020.02.008] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 02/03/2020] [Accepted: 02/13/2020] [Indexed: 10/24/2022]
|
15
|
Alzabut J, Tyagi S, Martha S. On the stability and Lyapunov direct method for fractional difference model of BAM neural networks. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-179537] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Jehad Alzabut
- Department of Mathematics and General Sciences, Prince Sultan University, Riyadh, Saudi Arabia
| | - Swati Tyagi
- Department of Applied Sciences, Punjab Engineering College (PEC), Deemed to be University, Chandigarh, India
- Department of Mathematics, Indian Institute of Technology Ropar, Rupnagar, Punjab, India
| | - S.C. Martha
- Department of Mathematics, Indian Institute of Technology Ropar, Rupnagar, Punjab, India
| |
Collapse
|
16
|
You X, Song Q, Zhao Z. Existence and finite-time stability of discrete fractional-order complex-valued neural networks with time delays. Neural Netw 2020; 123:248-260. [DOI: 10.1016/j.neunet.2019.12.012] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2019] [Revised: 11/28/2019] [Accepted: 12/10/2019] [Indexed: 10/25/2022]
|
17
|
Liu Y, Shen B, Shu H. Finite-time resilient H∞ state estimation for discrete-time delayed neural networks under dynamic event-triggered mechanism. Neural Netw 2020; 121:356-365. [DOI: 10.1016/j.neunet.2019.09.006] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2019] [Revised: 08/26/2019] [Accepted: 09/05/2019] [Indexed: 10/25/2022]
|
18
|
Ding Z, Zeng Z, Zhang H, Wang L, Wang L. New results on passivity of fractional-order uncertain neural networks. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.03.042] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
19
|
Liu W, Jiang M, Yan M. Stability analysis of memristor-based time-delay fractional-order neural networks. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2018.09.073] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
|
20
|
Global Mittag–Leffler projective synchronization of nonidentical fractional-order neural networks with delay via sliding mode control. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.06.029] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
21
|
Global Mittag-Leffler stability and synchronization analysis of fractional-order quaternion-valued neural networks with linear threshold neurons. Neural Netw 2018; 105:88-103. [DOI: 10.1016/j.neunet.2018.04.015] [Citation(s) in RCA: 89] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2017] [Revised: 02/12/2018] [Accepted: 04/20/2018] [Indexed: 11/15/2022]
|
22
|
Yang Y, Liao X, Dong T. Period-adding bifurcation and chaos in a hybrid Hindmarsh–Rose model. Neural Netw 2018; 105:26-35. [DOI: 10.1016/j.neunet.2018.04.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Revised: 03/08/2018] [Accepted: 04/10/2018] [Indexed: 11/30/2022]
|
23
|
Wang LF, Wu H, Liu DY, Boutat D, Chen YM. Lur’e Postnikov Lyapunov functional technique to global Mittag-Leffler stability of fractional-order neural networks with piecewise constant argument. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.03.050] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
|
24
|
Wan L, Wu A. Multistability in Mittag-Leffler sense of fractional-order neural networks with piecewise constant arguments. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.01.049] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
|
25
|
Li L, Li C. Discrete Analogue for a Class of Impulsive Cohen–Grossberg Neural Networks with Asynchronous Time-Varying Delays. Neural Process Lett 2018. [DOI: 10.1007/s11063-018-9819-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
26
|
New Algebraic Criteria for Global Exponential Periodicity and Stability of Memristive Neural Networks with Variable Delays. Neural Process Lett 2018. [DOI: 10.1007/s11063-018-9803-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
27
|
Bao G, Zeng Z, Shen Y. Region stability analysis and tracking control of memristive recurrent neural network. Neural Netw 2018; 98:51-58. [DOI: 10.1016/j.neunet.2017.11.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2017] [Revised: 10/05/2017] [Accepted: 11/02/2017] [Indexed: 10/18/2022]
|
28
|
Tao B, Xiao M, Sun Q, Cao J. Hopf bifurcation analysis of a delayed fractional-order genetic regulatory network model. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2017.09.018] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
29
|
Tan M, Pan Q. Global stability analysis of delayed complex-valued fractional-order coupled neural networks with nodes of different dimensions. INT J MACH LEARN CYB 2017. [DOI: 10.1007/s13042-017-0767-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
|
30
|
Liu Y, Liu W, Wu Y. Associative Memory Realized by Reconfigurable Coupled Three-Cell CNNs. Neural Process Lett 2017. [DOI: 10.1007/s11063-017-9749-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
31
|
Zha J, Huang H, Huang T, Cao J, Alsaedi A, Alsaadi FE. A general memristor model and its applications in programmable analog circuits. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2017.04.057] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
|
32
|
Liu S, Yu Y, Zhang S. Robust synchronization of memristor-based fractional-order Hopfield neural networks with parameter uncertainties. Neural Comput Appl 2017. [DOI: 10.1007/s00521-017-3274-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
33
|
Chen L, Cao J, Wu R, Tenreiro Machado J, Lopes AM, Yang H. Stability and synchronization of fractional-order memristive neural networks with multiple delays. Neural Netw 2017; 94:76-85. [DOI: 10.1016/j.neunet.2017.06.012] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2017] [Revised: 04/11/2017] [Accepted: 06/22/2017] [Indexed: 11/29/2022]
|
34
|
Stability Analysis for Memristive Recurrent Neural Network Under Different External Stimulus. Neural Process Lett 2017. [DOI: 10.1007/s11063-017-9671-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
|
35
|
Liu L, Wu A, Zeng Z, Huang T. Global mean square exponential stability of stochastic neural networks with retarded and advanced argument. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2017.03.057] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
|
36
|
Hu C, Yu J, Chen Z, Jiang H, Huang T. Fixed-time stability of dynamical systems and fixed-time synchronization of coupled discontinuous neural networks. Neural Netw 2017; 89:74-83. [DOI: 10.1016/j.neunet.2017.02.001] [Citation(s) in RCA: 142] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2016] [Revised: 01/10/2017] [Accepted: 02/01/2017] [Indexed: 10/20/2022]
|