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Passivity and Synchronization for Fuzzy Coupled Reaction-Diffusion Neural Networks With Multiweights.
This study introduces adaptive control methods to ensure passivity and synchronization in fuzzy coupled reaction-diffusion neural networks (FCRDNNs). These techniques are validated for networks with multistate or spatial-diffusion couplings.
Area of Science:
- Control Theory
- Artificial Neural Networks
- Nonlinear Dynamics
Background:
- Fuzzy coupled reaction-diffusion neural networks (FCRDNNs) are complex systems requiring robust control strategies.
- Ensuring network passivity and synchronization is crucial for their stability and reliable operation.
- Existing methods may not fully address the challenges posed by multistate or spatial-diffusion couplings.
Purpose of the Study:
- To develop and validate adaptive control schemes for achieving passivity and synchronization in FCRDNNs.
- To investigate passivity and synchronization for FCRDNNs with both multistate and spatial-diffusion couplings.
- To provide theoretical criteria and practical validation for the proposed control strategies.
Main Methods:
- Adaptive state feedback control is employed to design controllers.
- Lyapunov functional method is utilized to analyze system stability and derive control conditions.
- Passivity criteria and synchronization conditions are mathematically formulated.
- Numerical simulations are conducted to demonstrate the effectiveness of the proposed methods.
Main Results:
- Several passivity criteria are derived for FCRDNNs with multistate couplings using adaptive state feedback control.
- A sufficient condition for guaranteeing synchronization in multistate coupled FCRDNNs is established.
- The adaptive control technique and Lyapunov functional method successfully address passivity and synchronization for FCRDNNs with spatial-diffusion couplings.
- Numerical examples confirm the efficacy of the developed adaptive control schemes.
Conclusions:
- The proposed adaptive control schemes effectively achieve passivity and synchronization in FCRDNNs with diverse coupling structures.
- The study provides a theoretical framework and practical validation for controlling complex neural network dynamics.
- The findings contribute to the advancement of robust control strategies for distributed parameter systems.
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