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Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
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Preassigned-time synchronization for complex-valued memristive neural networks with reaction-diffusion terms and
Hongliang Liu1, Jun Cheng2, Jinde Cao3
1School of Mathematics and Physics, University of South China, Hengyang, 421001, PR China.
Summary
This research achieves preassigned-time synchronization for complex memristive neural networks using novel control strategies. The study ensures synchronization within a user-defined timeframe, offering precise control over settling times.
Area of Science:
- Complex-valued neural networks
- Nonlinear control systems
- Dynamical systems theory
Background:
- Memristive neural networks (MNNs) are crucial for advanced computing.
- Achieving synchronization in MNNs with reaction-diffusion terms is challenging.
- Existing synchronization methods often lack precise control over settling times.
Purpose of the Study:
- To develop a preassigned-time stable control strategy for complex-valued MNNs.
- To design controllers that guarantee synchronization within a user-specified time.
- To address limitations in settling time specification and activation function constraints.
Main Methods:
- Design of two distinct preassigned-time controllers with varying power exponents.
- Application of Green's formula and boundary conditions to handle symmetry.
- Analysis of synchronization for complex-valued MNNs with Markov parameters and reaction-diffusion terms.
Main Results:
- Synchronization of complex-valued MNNs is achieved within a preassigned time.
- The proposed control strategy allows for a priori specification of the settling time.
- The method is effective even with relaxed constraints on the activation function and potential symmetry loss.
Conclusions:
- The study successfully demonstrates preassigned-time synchronization for complex-valued MNNs.
- The proposed controllers offer superior flexibility in setting synchronization deadlines.
- The findings advance the control theory for complex dynamical systems.
Keywords:
Complex-valuedMarkovian jump parametersPreassigned-time synchronizationReaction–diffusion neural networksMore Related Videos
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