Abstract
This work generalizes the projection scaling factor to a general constant matrix and proposes the matrix-projection synchronization (MPS) for fractional-order neural networks (FNNs) based on sliding mode control firstly. This kind of scaling factor is far more complex than the constant scaling factor, and it is highly variable and difficult to predict in the process of realizing the synchronization for the driving and response systems, which can ensure high security and strong confidentiality. Then, the fractional-order integral sliding surface and sliding mode controller for FNNs are designed. Furthermore, the criterion for realizing MPS is proved, and the reachability and stability of the synchronization error system are analyzed, so that the global MPS is realized for FNNs. Finally, a numerical application is given to demonstrate the feasibility of theory analysis. MPS is more general, so it is reduced to antisynchronization, complete synchronization, projective synchronization (PS), and modified PS when selecting different projective matrices. This work will enrich the synchronization theory of FNNs and provide a feasible method to study the MPS of other fractional-order dynamical models.
Highlights
Neural network is an important part of artificial intelligence, which is composed of a large number of highly connected neurons
Integer order differential equations cannot describe the memory properties of neurons and the dependence on past history, but the fractional-order calculus [1,2,3,4], which has strong memory and hereditary characteristic, contains all of the information from the start point to the current moment and can describe the memory properties and dynamical behaviors of neurons more accurately. erefore, fractional-order neural networks (FNNs) can improve the computational ability of neurons, speed up the information transmission of neurons, and solve the problem of parameter identification effectively
Two systems are called projective synchronization (PS) when the drive and response systems are synchronized to a scaling factor
Summary
Neural network is an important part of artificial intelligence, which is composed of a large number of highly connected neurons. Journal of Mathematics surface, designed fractional-order sliding mode controller, and realized PS for two FNNs with different structures. In [26, 27], by means of the fractional Lyapunov-like method, the authors realized PS in finite-time and mixed H∞/passive projective synchronization for nonidentical FNNs via a new sliding mode controller. In [28], by using sliding mode control, Wu et al realized the finite-time interlayer PS of fractional-order two-layer networks based on Caputo derivates. Based on the characteristics of matrix scaling factor, our work presents a new kind of MPS for FNNs, whose complexity and unpredictability can effectively increase the difficulty for hackers to track the right path, improve the antiattack capability of the system, and enhance the confidentiality of secure communication.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
