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Delay-distribution-dependent state estimation for neural networks under stochastic communication protocol with
Jiahui Li1, Zidong Wang2, Hongli Dong1
1Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing 163318, China; Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Northeast Petroleum University, Daqing 163318, China.
Summary
This study introduces a new protocol for remote state estimation in artificial neural networks with random delays. It ensures stable estimation and minimizes data collisions using a stochastic communication protocol (SCP) and Markov chains.
Area of Science:
- Control Systems Engineering
- Artificial Intelligence
- Networked Systems
Background:
- Remote state estimation for artificial neural networks is challenging due to random time-varying delays.
- Data collisions in sensor-to-estimator communication channels can degrade performance.
- Stochastic communication protocols (SCP) offer a method to manage channel access.
Purpose of the Study:
- To develop a protocol-based remote state estimation method for delayed artificial neural networks.
- To address data collision issues using a stochastic communication protocol (SCP).
- To ensure asymptotic stability and H∞ performance for the estimation error dynamics.
Main Methods:
- Utilizing a stochastic communication protocol (SCP) governed by an uncertain Markov chain for sensor scheduling.
- Applying the Lyapunov-Krasovskii functional method combined with stochastic analysis techniques.
- Solving a convex optimization problem to determine the estimator parameters.
Main Results:
- A sufficient criterion for the existence of a remote state estimator was derived.
- The augmented estimation error dynamics are guaranteed to be asymptotically stable with a prescribed H∞ performance index.
- The proposed method effectively reduces data collisions and handles random delays.
Conclusions:
- The study successfully established a theoretical framework for robust remote state estimation in delayed artificial neural networks.
- The developed method, employing SCP and Markov chain analysis, provides a viable solution for improving estimation accuracy and stability.
- Numerical simulations validated the effectiveness of the proposed approach.
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