About this Research Topic
This research topic intends to bring together collaborative research regarding the stability, control and synchronization of uncertain Fractional Order (FO) neural networks and physiological networks. Neural networks have been increasingly implemented in the modelling and control of many complex dynamic systems. Issues like stability, control and synchronization are important to model these complex dynamic systems which are used to represent many physical systems such as electrical, mechanical, chemical, and biological. Meanwhile, chaotic physiological networks are a kind of complex dynamic model which represents chaotic nodes for physical systems such as solid-state devices, biological neural systems among other kinds of systems. It is, therefore, important to develop new control and synchronization strategies for this type of systems.
This Research Topic is calling for articles that present a stability analysis, control and synchronization of uncertain fractional order neural networks and physiological networks. Research studies focused on the topological analysis of uncertainties found in physiological networks are also welcome. Achieving the stability analysis of neural networks and physiological networks is necessary and essential for the implementation of the Lyapunov theory and other stability analysis for neural networks such as the Mittag-Leffler analysis. In the case of physiological networks, it is important to implement other analysis, such as bifurcation analysis which is important to comprehend the chaotic regimes in which the node of the fractional order uncertain chaotic neural network behaves. Besides it is important to synthesise control and synchronization strategies for chaotic fractional order uncertain neural networks and complex chaotic networks taking into consideration many kind of strategies such as sliding mode, passivity based, robust control etc. to achieve these objectives. It is important to mention that several phenomena such as saturation, time delays and dissipativity can be considered in the analysis of neural networks and complex chaotic neural networks.
It is important to consider that the topics are varied taking into consideration the vast amount of neural network types and complex chaotic systems. The list of specific topics may include but are not limited to as followed:
1. Stability of FO uncertain neural networks.
2. Stability of FO uncertain physiological networks.
3. Synchronization of coupled FO uncertain physiological networks.
4. Control of FO uncertain physiological networks.
5. FO Neural Control for robotic systems.
6. Synchronization of biological FO uncertain physiological networks.
7. Synchronization of FO uncertain complex neural networks.
8. Synchronization of quaternion neural networks.
9. FO uncertain neural control for electrical and power systems.
10. Topological analysis of uncertainties found in this kind of systems.
11. Robustness analysis.
12. Bifurcations of complex chaotic neural networks and other types of physiological networks.
This Research Topic is calling for articles that present a stability analysis, control and synchronization of uncertain fractional order neural networks and physiological networks. Research studies focused on the topological analysis of uncertainties found in physiological networks are also welcome. Achieving the stability analysis of neural networks and physiological networks is necessary and essential for the implementation of the Lyapunov theory and other stability analysis for neural networks such as the Mittag-Leffler analysis. In the case of physiological networks, it is important to implement other analysis, such as bifurcation analysis which is important to comprehend the chaotic regimes in which the node of the fractional order uncertain chaotic neural network behaves. Besides it is important to synthesise control and synchronization strategies for chaotic fractional order uncertain neural networks and complex chaotic networks taking into consideration many kind of strategies such as sliding mode, passivity based, robust control etc. to achieve these objectives. It is important to mention that several phenomena such as saturation, time delays and dissipativity can be considered in the analysis of neural networks and complex chaotic neural networks.
It is important to consider that the topics are varied taking into consideration the vast amount of neural network types and complex chaotic systems. The list of specific topics may include but are not limited to as followed:
1. Stability of FO uncertain neural networks.
2. Stability of FO uncertain physiological networks.
3. Synchronization of coupled FO uncertain physiological networks.
4. Control of FO uncertain physiological networks.
5. FO Neural Control for robotic systems.
6. Synchronization of biological FO uncertain physiological networks.
7. Synchronization of FO uncertain complex neural networks.
8. Synchronization of quaternion neural networks.
9. FO uncertain neural control for electrical and power systems.
10. Topological analysis of uncertainties found in this kind of systems.
11. Robustness analysis.
12. Bifurcations of complex chaotic neural networks and other types of physiological networks.
Keywords: Network physiology, neural networks, Stability, Synchronization, stabilization, control, chaotic complex networks, Fractional order calculus
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
