Design of H∞ State Estimation Algorithm for Delayed Memristive Neural Networks with Variance Constraints and DoS Attack under Encoding-Decoding Scheme

This paper considers the design of varianceconstrained H_{\infty} state estimation algorithm for delayed memristive neural networks subject to DoS attack under encodingdecoding scheme. To ensure the security of data transmission, an encoding-decoding communication mechanism is utilized in the transm...

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Veröffentlicht in:2025 7th International Conference on Data-driven Optimization of Complex Systems (DOCS) S. 93 - 98
Hauptverfasser: Li, Jiaying, Gao, Yan, Du, Junhua, Li, Jiaxing, Liu, Sitong, Ali, Mahliya, Pazlamu, Halisa
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 19.08.2025
Schlagworte:
Complex systems
Data security
DoS attack
Encoding
encoding-decoding scheme
H∞ performance
Memristive neural networks
Neural networks
Optimization
Performance analysis
State estimation
Sufficient conditions
Upper bound
variance constraint
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Zusammenfassung:This paper considers the design of varianceconstrained H_{\infty} state estimation algorithm for delayed memristive neural networks subject to DoS attack under encodingdecoding scheme. To ensure the security of data transmission, an encoding-decoding communication mechanism is utilized in the transmission channel, which is capable of encoding the transmitted data with a specified number of bits. Our aim is to propose a new H_{\infty} state estimation algorithm, where the error variance boundedness and H_{\infty} performance requirement are both guaranteed through the establishment of certain sufficient conditions. In the end, a simulation experiment is conducted to validate the effectiveness and practicality of the newly presented H_{\infty} state estimation algorithm.
DOI:10.1109/DOCS67533.2025.11200602